Systems, methods, and computer instructions are provided. The method includes retrieving a first set of a media content transmitted by a plurality of interaction clients based on a chronological order, wherein the first set of media content has been saved as part of communications of ephemeral messages between at least two users of the plurality of interaction clients. The method further includes creating a visual representation of the first set of media content, and causing to display, on at least one of the plurality of interaction clients, the visual representation the first set of media content.
H04L 51/52 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel pour la prise en charge des services des réseaux sociaux
2.
DESIGNATING A SELF-IMAGE FOR USE IN AN INTERACTION FUNCTION
A method includes determining participation in an interaction function by a first user of an interaction system with a second user of the interaction system. The method also includes accessing profile data of the first user, and determining, based on the profile data, whether the first user has captured or designated a first-user self-image for use in the interaction function. In response to determining that the first user has not captured or designated the first-user self-image, the method includes accessing a media content item that includes a character, identifying a head portion of the character in the media content item, replacing the head portion with a placeholder space, and displaying the media content item with the placeholder space in a user interface corresponding to the interaction function.
A neural network processor is designed to process sequential windows (W1, W2,... Wn) of a time-dependent signal. Each window contains multiple samples (ns) of the signal over a time interval (T), with each window shifted relative to the previous one by a time-step (ΔT) smaller than T. This time-step corresponds to a base shift amount (h) defined by h=[ΔT/T ns]. The processor executes a neural network with multiple layers (L), each containing a plurality of neurons (N(...,Y,...L)), indexed by time domain (Y). It performs operations for each sample window, including computing a differential result signal for a neuron by referencing a neuron with an index value (Y) determined by the base shift amount and an accumulated up/down-sampling factor S.
G06N 3/063 - Réalisation physique, c. à d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
G06F 7/483 - Calculs avec des nombres représentés par une combinaison non linéaire de nombres codés, p.ex. nombres rationnels, système de numération logarithmique ou nombres à virgule flottante
G06N 3/063 - Réalisation physique, c. à d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
Methods and systems are disclosed for enhancing or modifying an image by a diffusion model. The methods and systems receive a first image depicting a real-world scene including a target object and receive input associated with adjusting a zoom level of the first image. The methods and systems, in response to receiving the input, modify the zoom level associated with the first image to generate a second image having a view of the target object that is different from a view of the target object in the first image. The methods and systems analyze the second image using a generative machine learning model to generate an artificial image that modifies portions of the second image to improve the view of the target object relative to the second image.
Described is a system for identifying content augmentations based on an interaction function initiated by a user by determining an initiation of an interaction function from a first user of an interaction system, processing data associated with the interaction function using a first machine learning model to generate a feature vector, and identifying at least one recommended content augmentation based on a comparison of the feature vector for the interaction function to a feature vector for the at least one recommended content augmentation. The system then displays the at least one recommended content augmentation to the first user with a corresponding selectable user interface element for individual recommended content augmentations.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
G06V 20/20 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les scènes de réalité augmentée
7.
GENERATING GROUND TRUTHS FOR GENERATIVE AI APPLICATIONS
A first neural network is trained to generate a ground truth using a small set of example images that illustrate the goal ground truth output images, which can be full-body images of people in an AR style. The first neural network is used to generate ground truth output images from random input images. Example methods of the first neural network include determining poses in input images, changing values of pixels within areas of the input images, inputting the poses, the areas of the changed input images, and a text prompt describing the input images, into a neural network, to generate output images. The methods further include determining losses between the output images and the input images and updating weights of the neural network based on the losses. A second neural network is then trained using the generated ground truth. And, an application is generated that uses the second neural network.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06V 40/10 - Corps d’êtres humains ou d’animaux, p.ex. occupants de véhicules automobiles ou piétons; Parties du corps, p.ex. mains
A stepped diffraction grating and method for manufacturer thereof are disclosed. A plurality of parallel grating lines are each formed on a substrate surface by forming a plurality of stacked layers of optically transmissive material. In cross-section, each grating line has an upper layer having an upper surface having a first end and a second end; a bottom layer having a bottom surface abutting the substrate surface and an upper surface having a first end and a second end; a rising staircase portion extending at a rising staircase angle between 10 degrees and 60 degrees; and a falling staircase portion extending at a falling staircase angle between the rising staircase angle and 89 degrees.
A technique for deriving a mashup is described. Given a collection of media content items for sequential playback, a subset of the media content items are selected for inclusion in a mashup, based on selection criteria specified in a template associated with the featured story. The subset of media content items are then arranged in a mashup, which is prepended to the story. By automatically generating a mashup - an abbreviated version of a story - the mashup will increase user engagement and encourage sharing, because the mashup condenses the content into a more digestible and captivating format. By using optimized content selection criteria, the mashup will include only the best and most impactful moments, highlights, or key elements of the story. The shorter version grabs the viewer's attention, maintaining their interest and prompting them to share the condensed experience with others, enticing them to discover the full story.
Systems and methods for generating static and articulated 3D assets are provided that include a 3D autodecoder at their core. The 3D autodecoder framework embeds properties learned from the target dataset in the latent space, which can then be decoded into a volumetric representation for rendering view-consistent appearance and geometry. The appropriate intermediate volumetric latent space is then identified and robust normalization and de-normalization operations are implemented to learn a 3D diffusion from 2D images or monocular videos of rigid or articulated objects. The methods are flexible enough to use either existing camera supervision or no camera information at all – instead efficiently learning the camera information during training. The generated results are shown to outperform state-of-the-art alternatives on various benchmark datasets and metrics, including multi-view image datasets of synthetic objects, real in-the-wild videos of moving people, and a large-scale, real video dataset of static objects.
A chatbot system for an interactive platform is disclosed. The chatbot system retrieves a conversation history of one or more conversations between a user and a chatbot from a conversation history datastore and generates one or more summarized memories using the conversation history. One or more moderated memories are generated using the summarized memories. The moderated memories are stored in a memories datastore. A user prompt is received, and a current conversation context is generated from a current conversation between the user and the chatbot. One or more memories are retrieved from the memories datastore using the current conversation context. An augmented prompt is generated using the user prompt and the one or more memories, which is communicated to a generative Al model. A response is received from the generative Al model to the augmented prompt, which is provided to the user.
G06F 40/35 - Représentation du discours ou du dialogue
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p.ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/216 - Gestion de l'historique des conversations, p.ex. regroupement de messages dans des sessions ou des fils de conversation
Methods and systems are disclosed for applying machine learning models to compressed videos. The system receives a video, depicting an object, that has previously been compressed using one or more video compression processes. The system analyzes, using one or more machine learning models, the video that has previously been compressed to generate a prediction corresponding to the object depicted in the video, with one or more artifacts resulting from application of the one or more machine learning models to the video that has been previously compressed being absent from the prediction. The system generates a visual output based on the prediction in which the one or more artifacts are absent.
G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
H04N 19/86 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le pré-traitement ou le post-traitement spécialement adaptés pour la compression vidéo mettant en œuvre la diminution des artéfacts de codage, p.ex. d'artéfacts de blocs
13.
GENERATIVE NEURAL NETWORKS FOR STYLIZING MEDIA CONTENT
A mobile application with an improved user interface facilitates generating stylized media content items including images and videos. An end-user selects a desired visual effect from a set of options. The mobile application captures or accesses an image. The image is processed on a server using a generative neural network pre-trained to apply stylizations based on the selected effect. The server sends back the stylized image to the mobile application for display. The end-user can then save the stylized image or generate a video (e.g., an animation) showing the original image transition to the stylized image. The user interface provides an efficient creative workflow to apply aesthetic enhancements in a visual style chosen by the end-user. Generative machine learning techniques automate stylization to enable accessible media, customization and sharing.
G06F 3/04845 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs pour la transformation d’images, p.ex. glissement, rotation, agrandissement ou changement de couleur
An optical device for use in an augmented reality or virtual reality display, comprising: a planar waveguide; an input diffractive optical element, DOE, configured to receive light from a projector and couple the light into the waveguide; the output DOE configured to receive light from the input diffractive optical element in an input direction, expand the light in two dimensions and couple the light out of the waveguide towards a user, the output DOE comprising a first diffractive region configured to diffract light within the waveguide, the first diffractive region having a first direction of periodicity and a second direction of periodicity, wherein an angle between the input direction and the first direction of periodicity is equal and opposite to an angle between the input direction and the second direction of periodicity, wherein the first diffractive region comprises an array of optical structures, wherein each optical structure is oriented in a third direction that is non-parallel to the first and second directions of periodicity, and is configured to couple light out of the waveguide towards the user.
A method for manufacturing a liquid crystal (LC) display includes determining an amount of an LC material to be used in the LC display, determining a silane material to be mixed with the LC material and an amount of the silane material to be mixed with the LC material based on the LC material and the amount of the LC material, mixing the amount of the silane material with the amount of the LC material to generate an LC mixture, and heat treating the LC mixture in contact with a display substrate to bond at least a portion of the silane material to one or more surfaces of the display substrate, such that the silane material acts as a surfactant. The amount of the silane material may constitute at least 0.8% of the LC mixture by weight.
Described is a system for improving machine learning models. In some cases, the system improves such models by identifying a performance characteristic for machine learning model blocks in an iterative denoising process of a machine learning model, connecting a prior machine learning model block with a subsequent machine learning model block of the machine learning model blocks within the machine learning model based on the identified performance characteristic, identifying a prompt of a user, the prompt indicative of an intent of the user for generative images, and analyzing data corresponding to the prompt using the machine learning model to generate one or more images, the machine learning model trained to generate images based on data corresponding to prompts.
Described is a system for improving machine learning models by accessing a first latent diffusion machine learning model, the first latent diffusion machine learning model trained to perform a first number of denoising steps, accessing a second latent diffusion machine learning model that was derived from the first latent diffusion machine learning model, the second latent diffusion machine learning model trained to perform a second number of denoising steps, generating noise data, processing the noise data via the first latent diffusion machine learning model to generate one or more first images, processing the noise data via the second latent diffusion machine learning model to generate one or more second images, and modify a parameter of the second latent diffusion machine learning model based on a comparison of the one or more first images with the one or more second images.
A video montage is assembled by one or more processors by selecting a number of media items for use in the video montage from a collection of media items. An audio track having a theme parameter corresponding to a theme parameter of the number of media items is identified, and a video montage incorporating the media items and the audio track is generated. A data structure may specify an identity and order of the media items and a start location of the audio track, and the video montage may be created by generating individual video segments from each media item in the number of media items, and assembling the individual video segments into the video montage based on an order specified in the data structure. Updates or edits to the video montage are represented as changes to the data structure, which is used to generate an updated video montage.
Methods and systems are disclosed for generating mirrored 3D assets for an extended reality (XR) experience. The system receives a three-dimensional (3D) object comprising a target and analyzes the 3D object using one or more machine learning models to generate data associated with a mirrored version of the target of the 3D object. The system applies the mirrored version of the target to a mirrored version of the 3D object using the generated data and generats a new 3D object comprising the mirrored version of the 3D object and the mirrored version of the target.
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
20.
PROVIDING DRAGGABLE SHUTTER BUTTON DURING VIDEO RECORDING
Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for providing an draggable shutter button during video recording. The program and method provide for displaying a user interface within an application running on a device, the user interface presenting real-time image data captured by a camera of the device, the user interface including a shutter button which is configured to be selectable by a user to initiate video recording in response to a first user gesture; and upon detecting the first user gesture selecting the shutter button, initiating video recording with respect to the real-time image data, and providing for the shutter button to be draggable in predefined directions to perform respective functions related to the video recording.
H04N 23/62 - Commande des paramètres via des interfaces utilisateur
H04N 23/63 - Commande des caméras ou des modules de caméras en utilisant des viseurs électroniques
H04N 23/667 - Changement de mode de fonctionnement de la caméra, p. ex. entre les modes photo et vidéo, sport et normal ou haute et basse résolutions
H04N 23/45 - Caméras ou modules de caméras comprenant des capteurs d'images électroniques; Leur commande pour générer des signaux d'image à partir de plusieurs capteurs d'image de type différent ou fonctionnant dans des modes différents, p. ex. avec un capteur CMOS pour les images en mouvement en combinaison avec un dispositif à couplage de charge [CCD]
H04N 23/69 - Commande de moyens permettant de modifier l'angle du champ de vision, p. ex. des objectifs de zoom optique ou un zoom électronique
G06F 3/04817 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p.ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comport utilisant des icônes
G06F 3/04845 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs pour la transformation d’images, p.ex. glissement, rotation, agrandissement ou changement de couleur
G06F 3/0488 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p.ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p.ex. des gestes en fonction de la pression exer utilisant un écran tactile ou une tablette numérique, p.ex. entrée de commandes par des tracés gestuels
H04N 5/222 - TRANSMISSION D'IMAGES, p.ex. TÉLÉVISION - Détails des systèmes de télévision Équipements de studio
G11B 27/031 - Montage électronique de signaux d'information analogiques numérisés, p.ex. de signaux audio, vidéo
A lift reporting system to perform operations that include: accessing user behavior data associated with one or more machine-learned (ML) models, the ML models associated with identifiers; determining causal conversions associated with the ML models based on the user behavior data, the causal conversions comprising values; performing a comparison between the values that represents the causal conversions; determining a ranking of the ML models based on the comparison; and causing display of a graphical user interface (GUI) that includes a display of identifiers associated with ML models.
Described is a system for improving machine learning models. In some cases, the system improves such models by identifying an autoencoder for a latent diffusion machine learning model, the latent diffusion machine learning model is trained to receive text as input and output an image based on the received text. The system identifies a number of channels in a decoder of the autoencoder, the decoder being configured to receive latent features as input and output images. The system further identifies a performance characteristic of the decoder and changes the node topology of the decoder based on the performance characteristic to generate an updated decoder. The system retrains the latent diffusion machine learning model using the updated decoder by inputting latent features to the updated decoder, receiving an outputted image from the updated decoder, and updating one or more weights of the decoder based on an assessment of the outputted image.
Described is a system for improving machine learning models by accessing a first latent diffusion machine learning model, accessing a second latent diffusion machine learning model that was derived from the first latent diffusion machine learning model, the second latent diffusion machine learning model trained to perform a second number of denoising steps, generating noise data, processing the noise data via the first latent diffusion machine learning model to generate one or more first latent features, processing the noise data via the second latent diffusion machine learning model to generate one or more second latent features, and inputting the one or more first latent features and the one or more second latent features into a loss function. The system then modifies a parameter of the second latent diffusion machine learning model based on the output of the loss function.
A system includes one or more hardware processors and at least one memory storing instructions that cause the one or more hardware processors to perform operations including retrieving, via a client device, a first selection of a first media content captured by an interaction client included in the client device and retrieving, via the client device, a second selection of second media content captured by an application executable by the client device. The operations also include displaying the first selection of the first media content alongside the second selection of the second media content on a display of the client device, and receiving a user selection of the first selection of first media content, of the second selection of the second media content, or of a combination thereof. The operations additionally include providing the user selection as part of a message transmitted via the client device.
A system for generating avatars from user self-images is disclosed, whereby the system accesses a media content item of a user that includes a face of the user, analyzes data associated with the media content item using a first machine learning model to generate a first modified media content item, parses a portion of the first modified media content item corresponding to the face of the user, and analyzes data associated with the portion of the first modified media content item using a second machine learning model to generate a digital avatar for the user.
A63F 13/655 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p.ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu automatiquement par des dispositifs ou des serveurs de jeu, à partir de données provenant du monde réel, p.ex. les mesures en direct dans les compétitions de course réelles par importation de photos, p.ex. du joueur
G06T 5/60 - utilisant l’apprentissage automatique, p. ex. les réseaux neuronaux
A system includes one or more hardware processors and at least one memory storing instructions that cause the one or more hardware processors to perform operations including retrieving a first set of a media content captured by an interaction client included in a client device, and retrieving a second set of media content captured by the interaction client included in the client device. The operations also include assigning the first set of media content a first ranking value, and assigning the second set of media content a second ranking value, creating a first visual representation of the first set of media content and a second visual representation of the second set of the second set of media content based on the first ranking value and on the second ranking value, and causing to display, on a display of the client device, the first visual representation and the second visual representation.
Described is a system for performing a set of machine learning model training operations that include: accessing media content items associated with interaction functions initiated by users of an interaction system, generating training data including labels for the media content items, extracting features from a media content item of the media content items, identifying additional media content items to include in the training data based on the extracted features from the media content item, processing the training data using a machine learning model to generate a media content item output; and updating one or more parameters of the machine learning model based on the media content item output. The system checks whether retraining criteria has been met, and repeats the set of machine learning model training operations to retrain the machine learning model.
G06N 3/0442 - Réseaux récurrents, p.ex. réseaux de Hopfield caractérisés par la présence de mémoire ou de portes, p.ex. mémoire longue à court terme [LSTM] ou unités récurrentes à porte [GRU]
A head-wearable extended reality (XR) device comprises a frame and a light field display arrangement mounted to the frame. The light field display arrangement comprises a first display layer and a second display layer. At least one of the first display layer or the second display layer is selectively displaceable relative to the frame.
Methods and systems are disclosed for performing private identification matching. The methods and systems store, by a first entity, a first set of private identifiers associated with a first set of data. The methods and systems group, by the first entity, different subsets of the first set of private identifiers into respective buckets of a first plurality of buckets according to a grouping criterion. The methods and systems apply a function to a subset of a second set of data, stored by a second entity, corresponding to private identifiers associated with one or more buckets of a second plurality of buckets, grouped by the second entity, that match private identifiers associated with one or more buckets of the first plurality of buckets. The methods and systems provide, to the first entity, a result of applying the function to the subset of the second set of data.
Methods and systems are disclosed for generating an extended reality try-on experience based on an image produced by a diffusion model (530). The system receives a first image (620) depicting a real-world object (622) and receives a second image (610) depicting a target fashion item (612). The system generates a warped image (660) in which pixels of the target fashion item depicted in the second image replace pixels of a portion of the real-world object in the first image and generates one or more segmentation maps corresponding to incomplete portions of the warped image. The system analyzes the warped image and the one or more segmentation maps using a generative machine learning model (530) to generate an artificial image (670) that populates the incomplete portions of the warped image to depict the real-world object wearing the target fashion item.
G06V 10/26 - Segmentation de formes dans le champ d’image; Découpage ou fusion d’éléments d’image visant à établir la région de motif, p.ex. techniques de regroupement; Détection d’occlusion
G06V 20/20 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les scènes de réalité augmentée
G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
Aspects of the present disclosure involve a system comprising a storage medium storing a program and method for predicting a conversion rate. The program and method provide for receiving, from an advertisement service, a bid to display a first advertisement at a computing device; determining, in response to receiving the bid, a set of features that relate to the first advertisement; providing the set of features to a machine learning model configured to output a predicted conversion rate for the first advertisement, the machine learning model having been trained based on multi-task learning using plural sets of features corresponding to plural second advertisements, the plural sets of features being associated with both click-through conversions and view-through conversions; and determining, based on the output of the machine learning model with respect to the set of features, the predicted conversion rate for the first advertisement.
Described herein are techniques for facilitating the communication of text-based messages between end-users who are using messaging applications executing on client¬ based computing devices with different capabilities. Specifically, the messaging system described herein enables a first end-user to add a message element to a text-based message, which, when received by a message recipient using an augmented reality messaging application, will cause a 3-D avatar representing the message sender, to animate in accordance with a specific avatar animation associated with the message element. The message element may be an emoji, or a special sequence of characters, and may be a visible or invisible (e.g., meta-data) element of the text-based message.
H04L 51/043 - Messagerie en temps réel ou quasi en temps réel, p.ex. messagerie instantanée [IM] en utilisant ou en gérant les informations de présence
H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p.ex. messagerie instantanée [IM]
H04L 51/066 - Adaptation de format, p.ex. conversion de format ou compression
A method for manipulating a gallery of extended reality (XR) media items displayed in a field of view of the head-worn device. The gallery of XR media items is associated with an anchor user interface element. User selection input is detected at a perceived location of the anchor user interface element. Subsequent user motion input moves the perceived location or orientation of the gallery of XR media items in the field of view of the head-worn device. Rotation or translation of the anchor user interface elements results in a corresponding rotation and/or translation of all of the items in the gallery of XR media items.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/04815 - Interaction s’effectuant dans un environnement basé sur des métaphores ou des objets avec un affichage tridimensionnel, p.ex. modification du point de vue de l’utilisateur par rapport à l’environnement ou l’objet
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus
34.
SELECTIVE TESTING OF PRE-COMPILED EXTENDED REALITY OPERATING SYSTEMS
The disclosed examples provide a method that includes identifying a list of revisions for an Extended Reality (XR) operating system and dividing the list of revisions to set a currently selected revision. An already-compiled version of the currently selected revision of the XR operating system is retrieved and installed on an XR device. The method also includes initiating a test script for the XR device to test the XR operating system and receiving results of the test script. The method of dividing the list of revisions, retrieving and installing the already-compiled version, and initiating and receiving results of the test script is repeated until a revision that passes the test script is found to be adjacent to a revision on the list that fails the test script.
Described herein is a messaging application that executes on a wearable augmented reality device. The messaging application facilitates the anchoring or pinning of a 3-D avatar representing another end-user. An end-user wearing the AR device facilitates messaging with the other end-user via interactions with the 3-D avatar representing the other end-user. As such, the AR device processes various sensor inputs to detect when the end-user wearing the AR device is "targeting" the 3-D avatar, and enables an audio recording device to record an audible message for communicating to the other end-user.
A method for transferring a media item from a portable device to a head-worn device for display in an augmented reality mood board comprises receiving user input at the portable device to transfer the media item from the portable device to the head-worn device, and transmitting, via a short-range data transmission protocol, a low-resolution representation of the media item, from the portable device to the head-worn device. A link to a higher-resolution representation may also be transmitted to the head-worn device, to enable the head-worn device to obtain the higher-resolution representation. The transmission of the low-resolution representation may commence as soon as initiation of the gesture is detected by the portable device.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/04815 - Interaction s’effectuant dans un environnement basé sur des métaphores ou des objets avec un affichage tridimensionnel, p.ex. modification du point de vue de l’utilisateur par rapport à l’environnement ou l’objet
G06F 3/04883 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p.ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p.ex. des gestes en fonction de la pression exer utilisant un écran tactile ou une tablette numérique, p.ex. entrée de commandes par des tracés gestuels pour l’entrée de données par calligraphie, p.ex. sous forme de gestes ou de texte
G06F 3/14 - Sortie numérique vers un dispositif de visualisation
A system is disclosed, including a processor and a memory. The memory stores instructions that, when executed by the processor, configure the system to perform operations. Surface plane information is obtained, defining a surface plane passing through a surface location and oriented according to a surface normal. An edge is detected in an image. Virtual content is presented, having a virtual position based on an orientation of the edge and the surface plane information.
Described is a system for gathering interaction data from use of one or more interaction functions by a first user, wherein the interaction data includes data in different modalities and generating a multimodal memory for the interaction data by applying the interaction data to a first machine learning model. The system also identifies a prompt for the first user and processes a combination of data associated with the prompt and the multimodal memory using a second machine learning model to generate recommended content for the first user. The system then proceeds to apply the recommended content to a first interaction client of the first user.
Described is a system for dynamically applying model adaptations customized for individual users by detecting an image of a first real-world object from a camera feed, detecting landmarks on the first real-world object, and processing the landmarks on the first real -world object using a generative machine learning model to generate a first custom image template for the first real -world object where portions of the first custom image template are populated with visual content placed based on the first custom image template. The system then applies a content augmentation based on the first custom image template to the camera feed.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
Methods and systems are disclosed for operating an extended reality (XR) experience using one or more machine learning models. The methods and systems accessing, by an interaction application, an XR application and receive, by the interaction application, a query that defines one or more attributes of the XR application. The methods and systems generate a prompt for a generative machine learning model using the query and process the prompt using the generative machine learning model to generate one or more data objects that match the one or more attributes defined by the query. The methods and systems generate, using the XR application, one or more XR objects based on the one or more data objects generated by the generative machine learning model responsive to the prompt.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
A63F 13/53 - Commande des signaux de sortie en fonction de la progression du jeu incluant des informations visuelles supplémentaires fournies à la scène de jeu, p.ex. en surimpression pour simuler un affichage tête haute [HUD] ou pour afficher une visée laser dans un jeu de tir
G06F 3/04817 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p.ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comport utilisant des icônes
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
Apparatuses, computer readable medium, and methods for image capturing while circumnavigating objects using mobile devices are disclosed. Example methods include capturing an image, processing the image to identify an object within the image, determining a path around the object and a number of images to capture of the object, dividing the path by the number of images to determine a number of waypoints, and navigating the mobile device to the waypoints and capturing an image of the object at each waypoint of the waypoints. Examples include a person pointing at an object and the mobile device identifying the object based on the person pointing at the object. The mobile device determines a bounding box and a geometric center of the bounding box to determine the path to circumnavigate the object. The mobile device determines a height above a ground to assist in navigation.
Systems, methods, and computer readable media for 3D content display using head-wearable apparatuses. Example methods include a head-wearable apparatus that is configured to determine a position for a content item on a closest curved line, of a plurality of curved lines, to the head-wearable apparatus that has space for the content item. The method includes adjusting a shape of the content item based on the position of the content item on the closest curved line and a user view of a user of the head-wearable apparatus. The method includes causing the adjusted content item to be displayed on a display of the head-wearable apparatus at the position on the closest curved line. The curved lines are either higher or lower as the curved lines goes away from the head-wearable apparatus. Additionally, the curved line or the content item may be adjusted with a random movement for an organic appearance.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p.ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comport
G06F 3/04815 - Interaction s’effectuant dans un environnement basé sur des métaphores ou des objets avec un affichage tridimensionnel, p.ex. modification du point de vue de l’utilisateur par rapport à l’environnement ou l’objet
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
Methods and systems are disclosed for generating an extended reality (XR) try-on experience. The methods and systems store, in a multimodal memory, interaction data representing use of one or more interaction functions including data in different modalities. The methods and systems detect an object depicted in an image captured by an interaction client and generate, by a machine learning model, a prompt based on the object depicted in the image and the interaction data in the multimodal memory. The methods and systems generate an artificial texture based on the prompt and modify a texture of the object depicted in the image using the artificial texture that has been generated based on the prompt.
Aspects of the present disclosure involve a system comprising a storage medium storing a program and method for rule-based sharing of content collections. The program and method provide for storing, in association with each content collection, a set of rules with first criteria for adding a content item to the content collection, and with second criteria for viewing the content collection; determining, for a first content collection, that the respective first criteria is met for a first user of a first device; providing, based on the determining, for the first user to generate the content item; adding the generated content item to the first content collection; determining, for the first content collection, that the respective second criteria is met for a second user of a second device; and providing, based on the determining, the first content collection to the second device for viewing by the second user.
H04L 51/52 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel pour la prise en charge des services des réseaux sociaux
H04L 51/214 - Surveillance ou traitement des messages en utilisant le transfert sélectif
H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p.ex. messagerie instantanée [IM]
H04L 51/222 - Surveillance ou traitement des messages en utilisant des informations de localisation géographique, p.ex. des messages transmis ou reçus à proximité d'un certain lieu ou d'une certaine zone
Aspects of the present disclosure involve a system comprising a storage medium storing a program and method for providing augmented reality content in association with a live event. The program and method provide for accessing first live video provided to a media server, the first live video having been captured by a camera in association with a live event; accessing timeline data stored by the media server, the timeline data for synchronizing effects with respect to the first live video; causing, based on the timeline data, an output device to display the first live video with preselected augmented reality content; and providing, to a plurality of client devices, an indication of the timeline data, each client device being configured to capture respective second live video and to display the respective second live video together with the augmented reality content based on the indication of the timeline data.
An under-screen camera is provided. A camera is positioned behind a see-through display screen and positioned to capture scene image data of objects in front of the display screen. The camera captures scene image data of a real-world scene including a user. The scene image data is processed to remove artifacts in the scene image data created by capturing the scene image data through the see-through display screen such as blur, noise, backscatter, wiring effect, and the like.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
The disclosed methods and systems dynamically update a multimodal memory. The methods and systems generate a multimodal memory comprising interaction data including data in different modalities and add, at a first point in time, a first element to the multimodal memory representing a first attribute of a real -world object associated with a first set of data corresponding to a first modality. The methods and systems detect, at a second point in time, a second set of data corresponding to a second modality, the second set of data representing a second attribute of the real -world object and, in response, add a second element to the multimodal memory representing the second attribute of the real-world object.
Described is a system for overlaying visual content onto a real -world object by identifying a prompt of a user indicating a user's intent, accessing an image template, wherein the image template includes placement of features within the image template, and processing a combination of data associated with the image template and the prompt using a generative machine learning model to generate a first populated image template in which one or more portions of the image template are populated with visual content representing the user's intent. The system then proceeds to access an image depicting a real -world object and overlay the first populated image template that includes the visual content representative of the user's intent on at least a portion of the real -world object based on the placement of the features of the image template.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 40/16 - Visages humains, p.ex. parties du visage, croquis ou expressions
A system for including a chatbot into a group chat session is provided. The system receives a chatbot mention message from a user in the group chat session. The chatbot mention message includes a chatbot prompt for a chatbot. The system generates a prompt using the chatbot mention message and communicates the response as a chatbot response message to each user in the group chat session.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p.ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p.ex. messagerie instantanée [IM]
H04L 51/216 - Gestion de l'historique des conversations, p.ex. regroupement de messages dans des sessions ou des fils de conversation
H04L 12/18 - Dispositions pour la fourniture de services particuliers aux abonnés pour la diffusion ou les conférences
H04L 51/046 - Interopérabilité avec d'autres applications ou services réseau
An input video item that includes a target visual augmentation is accessed. A machine learning model uses the input video item to generate an embedding. The embedding may comprise a vector representation of a visual effect of the target visual augmentation. The machine learning model is trained, in an unsupervised training phase, to minimize loss between training video representations generated within each of a plurality of training sets. Each training set comprises a plurality of different training video items that each include the same predefined visual augmentation. Based on the generation of the embedding of the input video item, the target visual augmentation is mapped to an augmentation identifier.
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/783 - Recherche de données caractérisée par l’utilisation de métadonnées, p.ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
A method to enhance virtual audio capture in Augmented Reality (AR) experience recordings starts with a processor receiving a video from a camera that includes images of a real-world scene and an AR content item. Processor receives acoustic signals from microphones generate acoustic signals using real-world audio and speaker output that including AR audio of the AR content item. Processor receives an audio file associated with the AR audio of the AR content item and generates an enhanced audio using the acoustic signals and the audio file. Processor generates an enhanced video using the video and the enhanced audio. Other examples are described herein.
Methods and systems are disclosed for generating an extended reality (XR) try-on experience based on an image produced by a diffusion model. The system receives an image depicting a real -world object and generates a prompt comprising a textual description of a fashion item. The system analyzes the image and the textual description of the fashion item using a generative machine learning model to generate an artificial image that depicts an artificial object that resembles the real -world object wearing an artificial fashion item matching the textual description of the fashion item. The system identifies an object comprising a real -world product image that matches visual attributes of the artificial fashion item and replaces the artificial fashion item in the artificial image with the object to generate an output image.
G06V 10/75 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexte; Sélection des dictionnaires
G06T 11/60 - Edition de figures et de texte; Combinaison de figures ou de texte
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/20 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les scènes de réalité augmentée
An eyewear device including a sensor used to measure deformation of an eyewear frame to estimate an inter-pupillary distance (IPD) of an eyewear user. The sensor is used to determine head breadth (HB) of the user and to estimate the IPD of the user. A processor displays an image on a display of the eyewear as a function of the estimated IPD to improve virtual object rendering for an improved augmented reality (AR) viewing experience while reducing vergence accommodation mismatch (VAM). User profile data, such as age and gender, can be used to generate a more accurate estimated IPD of the user.
A61B 3/04 - Montures d'essai; Jeux de lentilles pour emploi dans ces montures
A61B 3/08 - Appareils pour l'examen optique des yeux; Appareils pour l'examen clinique des yeux du type à mesure subjective, c. à d. appareils de d’examen nécessitant la participation active du patient pour examen de vision binoculaire ou stéréoscopique, p.ex. pour le contrôle du strabisme
A61B 3/11 - Appareils pour l'examen optique des yeux; Appareils pour l'examen clinique des yeux du type à mesure objective, c. à d. instruments pour l'examen des yeux indépendamment des perceptions ou des réactions du patient pour mesurer la distance interpupillaire ou le diamètre de la pupille
Systems and methods herein describe generating a unified content feed accessible from within a conversation thread. The systems and methods access a chat session between users on a messaging platform receive an input from a select user from within the chat session, the input corresponding to a unified content feed comprising shared content relevant to all users of the chat session and personalized content relevant only to the select user, in response to receiving the input, replaces the user interface associated with the chat session with a user interface associated with the unified content feed, and displays the user interface associated with the unified content feed on a computer device associated with the select user.
H04L 51/52 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel pour la prise en charge des services des réseaux sociaux
55.
POWER ATTRIBUTION AND THROTTLING ON MOBILE DEVICES
Systems, methods, and computer readable media for power and temperature attribution on mobile devices. Example methods include granting a request for a resource, estimating an energy usage used by the resource for the application, where the estimating is based on a resource utilization and a resource usage duration, and in response to the power usage transgressing a power usage budget for the application, throttling the power usage of the application. The application may provide a module to be called for the application to reduce its power usage or temperature generation. The mobile device provides the application with the temperature generation and power usage of the application on a per resource used basis. The mobile device determines the power usage and the temperature generation of the resources of the mobile device. In some examples, the mobile device is an augmented reality (AR), virtual reality (VR), or mixed reality (MR) head-wearable device.
G06F 1/329 - Gestion de l’alimentation, c. à d. passage en mode d’économie d’énergie amorcé par événements Économie d’énergie caractérisée par l'action entreprise par planification de tâches
G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
G06F 1/3212 - Surveillance du niveau de charge de la batterie, p.ex. un mode d’économie d’énergie étant activé lorsque la tension de la batterie descend sous un certain niveau
56.
ANIMATABLE GARMENT EXTRACTION THROUGH VOLUMETRIC RECONSTRUCTION
Methods and systems are disclosed for generating an animatable garment from a single image. The system accesses a monocular image depicting a person wearing a fashion item and generates a three-dimensional (3D) mesh representing the person in a canonical space. The system determines a pose of the person depicted in the monocular image and modifies a pose of the 3D mesh to match the determined pose of the person. The system extracts a portion of the 3D mesh corresponding to the fashion item and generates an extended reality (XR) item using the extracted portion of the 3D mesh.
G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
57.
DEVICE-TO-DEVICE COLLOCATED AR USING HAND TRACKING
A method for aligning coordinate systems from separate handheld devices is described. In one aspect, the method includes accessing first pose data of a first handheld device, receiving second pose data of a second handheld device, detecting, from the first handheld device, hand-tracking data of a second user holding the second handheld device, and aligning a first coordinate system of the first handheld device with a second coordinate system of the second handheld device based on the first pose data, the second pose data, and the hand-tracking data of the second user holding the second handheld device.
G09G 5/38 - Dispositions ou circuits de commande de l'affichage communs à l'affichage utilisant des tubes à rayons cathodiques et à l'affichage utilisant d'autres moyens de visualisation caractérisés par l'affichage de dessins graphiques individuels en utilisant une mémoire à mappage binaire avec des moyens pour commander la position de l'affichage
Methods and systems are disclosed for generating an extended reality (XR) experience using a statically positioned device. The system receives, from a camera of a stationary device, an image depicting a real -world object, the camera being directed in a stationary manner towards a specified field of view of a real-world environment. The system analyzes the image using a machine learning model to predict tracking information for the real -world object, the machine learning model trained based on a plurality of training images depicting real -world objects in the specified field of view of the real-world environment and corresponding ground-truth tracking information for the real -world objects. The system selects an extended reality (XR) experience from a plurality of XR experiences and overlays one or more XR elements associated with the XR experience on the image based on the predicted tracking information to generate a modified image.
Methods and systems are disclosed for generating a body mesh from a single image. The system predicts both a volumetric reconstruction tensor of the monocular image and a pose of an object by applying a first machine learning model to a monocular image. The system identifies a portion of the pose of the object that corresponds to a point in a canonical space associated with a set of position encoding information. The system obtains a point of the volumetric reconstruction tensor corresponding to the identified portion of the pose. The system classifies the obtained point as being inside or outside of a canonical volume by applying a second machine learning model to the obtained point of the volumetric reconstruction tensor together with the set of position encoding information. The system generates a three-dimensional (3D) mesh representing the object in the canonical space.
Systems and methods herein describe generating a unified content feed accessible from within a conversation thread. The systems and methods access a chat session between a first user and a second user, access user data, activity data and personalized media content associated with each user, the media personalized content comprising public media content that is related to each user's user data and activity data, generates a shared dataset based on identifying common aspects between each user's user data and activity data, generates shared content comprising public media content that is related to shared dataset, and causes display of a unified content feed comprising the shared content and personalized content to each user's computer device.
Systems, methods, and computer readable media for continuous rendering are disclosed. Example methods include a head-wearable apparatus that is configured to continuously determine a position or pose of a user and then request graphics to be rendered from a remote rendering module based on the position or pose. If a current time is within a threshold of a presentation time, then the head-wearable apparatus selects rendered graphics received from the remote rendering module that are associated with a position or pose of the user that is closest in time to the presentation time. The selected rendered graphics are then adjusted to account for a difference between the render time and the presentation time. The adjusted rendered graphics are then presented to the user on a display of the head-wearable apparatus.
System and method for accessing, on a computing device, user location data and place data for each place of a plurality of places, the place data including check-in data such as locations of place-associated check-ins, and a check-in location distribution parameter computed based on the locations of place-associated check-ins. The system further computes a relevance score for each place of the plurality of places based on the user location data and the check-in data, ranks the plurality of places based on the respective relevance scores, and displays the ranking of the plurality of places at the computing device. Computing the relevance score can be further based on a distance between the user location and each place, or a count of place-associated check-ins over a predetermined period of time. The check-in location distribution parameter can be a Gaussian shape parameter.
A system includes one or more hardware processors and at least one memory storing instructions that cause the one or more hardware processors to perform operations including retrieving, via a server, a network graph of a plurality of users associated with a first user of a client device, and for each user in the plurality of users, deriving a set of user locations through a period in time. The instructions further include deriving a most visited location based on the set of user locations of each user, and displaying, on a display of the client device, a visual representation of the most visited location.
H04W 4/021 - Services concernant des domaines particuliers, p.ex. services de points d’intérêt, services sur place ou géorepères
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
G06Q 50/00 - Systèmes ou procédés spécialement adaptés à un secteur particulier d’activité économique, p.ex. aux services d’utilité publique ou au tourisme
Systems, methods, and computer readable media for augmented reality (AR) activated dispensing machines are disclosed. Example methods include a dispensing machine that is configured to access a captured image, process the image to generate an avatar of a user, present an image of the avatar with an indication of a vending item on the display of the system, determine the user performed a gesture to select the vending item, and dispense the selected vending item. Additionally, the dispensing machine is configured to provide virtual musical instruments to the user and record a music video. The dispensing machine is configured to communicate with a user device and send codes for vending items and AR graphics to be displayed to the user.
A head-wearable apparatus determines an imaginary reference plane intersecting a head of a user viewing augmented content in a viewing pane having vertical and lateral dimensions in a display of the head-wearable apparatus. The imaginary reference plane coincides with a first viewing direction of the head of the user. The apparatus detects a rotational movement of the head of the user in a vertical direction while viewing the augmented content. In response to the detected rotational movement, the apparatus determines a second viewing direction of the head of the user when viewing the augmented content in the second viewing direction and determines a reference angle between the imaginary reference plane and the second viewing direction. Based on the reference angle, the apparatus assign one of a billboard display mode and a headlock display mode (or combination) to the augmented content presented in the display.
G06F 3/04815 - Interaction s’effectuant dans un environnement basé sur des métaphores ou des objets avec un affichage tridimensionnel, p.ex. modification du point de vue de l’utilisateur par rapport à l’environnement ou l’objet
66.
NEAR FIELD COMMUNICATION FOR PAIRING WEARABLE DEVICES
A system includes a user device having a near field communication (NFC) interface. The system also includes a short-range wireless communication interface configured to communicate with devices in a local physical space. The system also includes one or more processors and a non-transitory computer readable storage medium storing instructions for detecting, via the NFC interface, near field proximity between the user device and a NFC- enabled device, receiving pairing information from the NFC-enabled device via the NFC interface, processing the pairing information to establish communication between the system and a wireless-enabled device located in the local physical space via the short-range wireless communication interface, receiving location information from the wireless-enabled device via the short-range wireless communication interface, and determining a location of the user device in the local physical space, relative to at least one reference point, using the location information.
H04W 4/02 - Services utilisant des informations de localisation
H04W 4/80 - Services utilisant la communication de courte portée, p.ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
H04W 76/14 - Gestion de la connexion Établissement de la connexion Établissement de la connexion en mode direct
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
H04W 84/12 - Réseaux locaux sans fil [WLAN Wireless Local Area Network]
An augmented reality system can include a waveguide having an edge surface that extends between opposing light-guiding surfaces. The waveguide can guide light toward the edge surface. The waveguide can include a reflectivity-reducing film, such as an absorptive film or a photovoltaic film, disposed on the edge surface. To form the reflectivity-reducing film, curable material can be disposed onto a dissolvable film. The curable material can be cured while disposed on the dissolvable film such that the cured material forms a reflectivity-reducing structure on the dissolvable film. The dissolvable film can be dissolved such that the reflectivity-reducing structure remains intact as a reflectivity-reducing film that can be adhered to the edge surface, such as with a primer layer. The edge surface can include nanostructures, sized smaller than half a wavelength of the guided light, that can reduce a reflectivity of the edge surface.
G02B 1/118 - Revêtements antiréfléchissants ayant des structures de surface de longueur d’onde sous-optique conçues pour améliorer la transmission, p.ex. structures du type œil de mite
G02B 5/00 - OPTIQUE ÉLÉMENTS, SYSTÈMES OU APPAREILS OPTIQUES Éléments optiques autres que les lentilles
A method for correcting bending of a flexible display device is described. The method includes forming a plurality of sensor groups of an augmented reality (AR) display device, where one of the plurality of sensor groups includes a combination of a camera, an IMU (inertial measurement unit), and a component, each being tightly coupled to each other, a spatial relationship between the camera, the IMU sensor, or the component being predefined, accessing sensor groups data from the plurality of sensor groups, estimating a spatial relationship between the plurality of sensor groups based on the sensor groups data, and displaying virtual content in a display of the AR display device based on the spatial relationship between the plurality of sensor groups.
The subject technology receives an object mesh, information related to a viewpoint for rendering an image of an object having a reflective surface, and a set of maps. The subject technology generates a rasterized RGB (Red Green Blue) image based on the object mesh, the viewpoint, and the set of maps. The subject technology generates, using a neural network model, an output image of the object with the reflective surface based at least in part on the rasterized RGB image and the viewpoint. The subject technology provides for display the output image of the object with the reflective surface on a display of a computer client device.
An extended Reality (XR) system that provides services for determining 3D data of physical objects in a real -world scene. The XR system receives a request from an application to initiate a spatial scan of a real-world scene. In response, the XR system captures video frame data of the real-world scene and captures a pose of the XR system. The XR system determines a physical object in the real -world scene and determines a 2D position of the physical object, using the video frame data. The XR system determines a depth of the physical object using the 2D position and determines a 3D position of the physical object in the real -world scene using the 2D position of the physical object, the depth of the physical object, and the pose of the XR system. The XR system communicates the 3D position data to the application.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/0346 - Dispositifs de pointage déplacés ou positionnés par l'utilisateur; Leurs accessoires avec détection de l’orientation ou du mouvement libre du dispositif dans un espace en trois dimensions [3D], p.ex. souris 3D, dispositifs de pointage à six degrés de liberté [6-DOF] utilisant des capteurs gyroscopiques, accéléromètres ou d’inclinaiso
G06V 20/20 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les scènes de réalité augmentée
Systems, devices, media, and methods are presented that provide an asynchronous task scheduling framework in which tasks are asynchronously scheduled for performance by a server system. Scheduling of a task is performed by marshalling the task (units of work converted to bytes) and storing the task in queues and retrieving the task from the queues and unmarshalling the retrieved tasks (bytes to units of work). The unmarshalled task is sent to a service (e.g., an email service) for performance. Examples of the approach introduce wrapping a hypertext transfer protocol (HTTP)/remote procedure call (RPC) call and brokering it transparently through a high-throughput message queue system.
Methods and systems are disclosed for generating a 3D body mesh. The system receives an image that includes a depiction of a real -world object in a real -world environment. The system applies a first machine learning model to a portion of the image that depicts the real-world object to predict a tensor of heatmaps representing vertex positions of a plurality of triangles of a 3D mesh corresponding to the real-world object. First and second heatmaps of the tensor represent respectively first and second groups of possible coordinates for a first vertex of a first triangle of the plurality of triangles. The system generates the 3D mesh based on the selected subset of the tensor of heatmaps.
A system for augmenting images using hand surface normal estimation is provided. In a model training phase, 3D models of hands are generated using 3D data of hands in a variety of positions. Target normal training data is generated that includes normals of surfaces of the 3D models and synthetic 2D image training data corresponding to the 3D models and the normals. The target normal training data and the synthetic image training data are used to train a normal estimation model. The normal estimation is used by an interactive application to generate augmentations that are applied to hand image data.
G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
Aspects of the present disclosure involve a system for performing ray tracing between augmented reality (AR) and real -world objects. The system accesses, by the mobile device, a video depicting a first object. The system obtains, by the mobile device, a three-dimensional (3D) model of the first object. The system applies, by the mobile device, a ray tracing process to the 3D model of the first object to estimate an optical effect on a portion of the first object relative to a second object that is depicted in the video. The system modifies a visual property of the portion of the first object based on the optical effect relative to the second object.
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
A system for generating extended reality effects using image data of hands and a depth estimation model. The depth estimation model is trained using pairings of synthetic 2D image data with sets of depths and segmentation masks. An extended reality system captures image data of hands in a real-world scene and uses the image data and the depth estimation model to generate the extended reality effects. The extended reality effects are provided to a user during an extended reality experience.
A media player providing real time rewind playback of a played media file having segments of frames. A last segment N of the played media file is cached and rendered on a device, such as a mobile device, then a previous segment N-1 is cached and rendered, and the process continues until there are no more segments of the played media file to cache and render. Only a segment of the played media file is cached at a time, rather than the whole media file, such that the played media file can be replayed on the fly.
H04N 21/431 - Génération d'interfaces visuelles; Rendu de contenu ou données additionnelles
H04N 21/44 - Traitement de flux élémentaires vidéo, p.ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène MPEG-4
H04N 21/4402 - Traitement de flux élémentaires vidéo, p.ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène MPEG-4 impliquant des opérations de reformatage de signaux vidéo pour la redistribution domestique, le stockage ou l'affichage en temps réel
H04N 21/472 - Interface pour utilisateurs finaux pour la requête de contenu, de données additionnelles ou de services; Interface pour utilisateurs finaux pour l'interaction avec le contenu, p.ex. pour la réservation de contenu ou la mise en place de rappels, pour la requête de notification d'événement ou pour la transformation de contenus affichés
H04N 21/845 - Structuration du contenu, p.ex. décomposition du contenu en segments temporels
77.
ADAPTIVE IMAGE PROCESSING FOR AUGMENTED REALITY DEVICE
Examples describe adaptive image processing for an augmented reality (AR) device. An input image is captured by at least one camera of the AR device, and a region of interest of the input image is determined. The region of interest is associated with an object that is being tracked using an object tracking system. A crop-and-scale order of an image processing operation directed at the region of interest is determined for the input image. One or more object tracking parameters may be used to determine the crop-and-scale order. The crop-and-scale order is dynamically adjustable between a first order and a second order. An output image is generated from the input image by performing the image processing operation according to the determined crop-and-scale order for the particular input image. The output image can be accessed by the object tracking system to track the object.
An amplifier circuit including a first folded double cascode stage configured to receive a differential input signal at a first pair of input transistors and generate a first drive signal, a second folded double cascode stage configured to receive the differential input signal at a second pair of input transistors and generate a second drive signal, and an output stage. The output stage includes a PMOS common-source output transistor configured to receive the first drive signal at its gate, and an NMOS common-source output transistor configured to receive the first drive signal at its gate, the PMOS common-source output transistor and NMOS common-source output transistor being jointly configured to generate an output signal based on the first drive signal and the second drive signal.
Examples disclosed herein describe prompt modification techniques for automated image generation. An image generation request comprising a base prompt is received from a user device. A plurality of prompt modifiers is identified. A processor-implemented scoring engine determines, for each prompt modifier, a modifier score. The modifier score for each prompt modifier is associated with the base prompt. One or more of the prompt modifiers are automatically selected based on the modifier scores. A modified prompt is generated. The modified prompt is based on the base prompt and the one or more selected prompt modifiers. The modified prompt is provided as input to an automated image generator to generate an image, and the image is caused to be presented on the user device.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/98 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos Évaluation de la qualité des motifs acquis
G06V 20/30 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p.ex. des photos ou des vidéos issus des réseaux sociaux
80.
SHORTCUTS FOR PRIVATE CONTENT COLLECTIONS SHARING WITHIN A MESSAGING SYSTEM
A method, includes causing, on a first device associated with a first user, a display of a carousel of a plurality of messaging shortcut icons in a messaging application, receiving, from the first device, a selected messaging shortcut icon of the plurality of messaging shortcut icons, the selected messaging shortcut icon indicating first user input to share a content collection between the first user and a second user associated with the selected messaging shortcut icon, the content collection includes at least one media content item, the second user corresponding to a contact of the first user within the messaging application, and in response to receiving the selected messaging shortcut icon, enabling a display of the content collection on a second device associated with the second user.
H04L 51/52 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel pour la prise en charge des services des réseaux sociaux
Examples disclosed herein describe techniques related to automated image generation in an interaction system. An image generation request is received from a first user device associated with a first user of an interaction system. The image generation request comprises a text prompt. In response to receiving the image generation request, an image is automatically generated by an automated text-to-image generator, based on the text prompt. The image is caused to be presented on the first user device. An indication of user input to select the image is received from the user device. In response to receiving the indication of the user input to select the image, the image is associated with the first user within the interaction system, and a second user of the interaction system is enabled to be presented with the image.
H04L 51/52 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel pour la prise en charge des services des réseaux sociaux
82.
ALIGNMENT OF USER DEVICES IN A SHARED EXTENDED REALITY ENVIRONMENT BASED ON HAND TRACKING
A method for aligning coordinate systems of user devices in an augmented reality system using somatic points of a user's hand as alignment markers. Images captured from multiple user devices are used to align the reference coordinate systems of the user devices to a common reference coordinate system. In some examples, user devices capture images of a hand of a user and use object recognition to identify somatic points as alignment markers. The somatic points of a user device are translated to a common reference coordinate system determined by another user device.
Examples disclosed herein describe aspect ratio conversion techniques for automated image generation. An image generation request comprising a prompt is received from a user device. A processor-implemented automated image generator may generate a first image based on the prompt. The first image has a first aspect ratio. According to some examples, a region of interest is determined in the first image, based on a prompt alignment indicator for the region of interest. The first image is then processed to obtain a second image. The processing includes an automatic cropping operation directed at the region of interest. The second image has a second aspect ratio that is different from the first aspect ratio. The second image is caused to be presented on the user device.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/32 - Normalisation des dimensions de la forme
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
Examples disclosed herein describe techniques for automatic image quality evaluation. A first set of images generated by a first automated image generator and a second set of images generated by a second automated image generator are accessed. A first machine learning model generates a first quality indicator for each image in the first set of images and the second set of images. A second machine learning model generates a second quality indicator for each image in the first set of images and the second set of images. Based on the generated indicators, a first image from the first set of images and a second image from the second set of images are automatically selected and compared. A first ranking of the first automated image generator and the second automated image generator is generated based on the comparison, and ranking data is caused to be presented on a device.
A method is disclosed for controlling a liquid crystal pulse width modulated display. A repetition period includes A group periods, each including B modulation intervals, each modulation interval spanning H unit durations and, except for the final modulation interval of the repetition period, a remainder unit duration. A desired number N of unit duration pulses are distributed into H unit duration pulses for each modulation interval, with remainder desired pulses distributed among the remainder unit durations of the modulation intervals. A drive sequence is generated, including one or more repetitions of the repetition period.
G09G 3/20 - Dispositions ou circuits de commande présentant un intérêt uniquement pour l'affichage utilisant des moyens de visualisation autres que les tubes à rayons cathodiques pour la présentation d'un ensemble de plusieurs caractères, p.ex. d'une page, en composant l'ensemble par combinaison d'éléments individuels disposés en matrice
A head-wearable apparatus includes a frame having a front piece configured to hold left and right lenses. A left temple is coupled to the front piece and a right temple coupled to the front piece. A camera system includes one or more cameras coupled to the front piece, one or more left peripheral cameras coupled to an outside surface of the frame, and one or more right peripheral cameras coupled to an outside surface of the frame. A left peripheral display is coupled to an inside surface of the frame. The left peripheral display is configured to receive and display input from the one or more left peripheral cameras. A right peripheral display is coupled to an inside surface of the frame. The right peripheral display is configured to receive and display input from the one or more right peripheral cameras.
Methods and systems are disclosed for transferring garments from a real -world object to a virtual object. The system receives, by a client device, an image that includes a depiction of a real -world object having a fashion item in a real -world environment. The system accesses a three- dimensional (3D) avatar model of a human and generates a graphic item corresponding to the fashion item being worn by the real -world object depicted in the image. The system modifies the 3D avatar model of the human based on the graphic item and presents the 3D avatar model that has been modified based on the graphic item within a view of the real-world environment on the client device.
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p.ex. d’êtres humains, d’animaux ou d’êtres virtuels
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
A method of correcting perspective distortion of a selfie image captured with a short camera-to-face distance by processing the selfie image and generating an undistorted selfie image appearing to be taken with a longer camera-to-face distance. A pre-trained 3D face GAN processes the selfie image, inverts the 3D face GAN to obtain improved face latent code and camera parameters, fine tunes a 3D face GAN generator, and manipulates camera parameters to render a photorealistic face selfie image. The processed selfie image has less distortion in the forehead, nose, cheek bones, jaw line, chin, lips, eyes, eyebrows, ears, hair, and neck of the face.
A system for correcting for frame bending of an augmented reality system is provided. A combination of strain gauges and visual inertial odometry is used to determine strains in the frame. An initial model between strain gauge measurements and actual frame spatial relationships is based on finite element analysis or calibration. During an initial visual inertial odometry data calculation phase, the augmented reality system calculates bending or strains of the frame using strain data from the strain gauges mounted to the frame. Subsequent visual inertial odometry data calculations are used to generate a corrected frame model of the frame. The corrected frame model is used for calculating corrected tracking data and corrected virtual overlays that are used to generate virtual overlays used in an AR experience provided by the augmented reality system.
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
A chatbot system for an interactive platform. The chatbot receives prompts in a form of interactive platform posts to the interactive platform, and generates responses in the form of the interactive platform posts. In an aspect, the chatbot system generates messages that are used on the interactive platform for group chats with multiple users and 1 : 1 chats with a single user. In another aspect, a user may customize a chatbot to create a personal chatbot having a persona. In some examples, prompts to the chatbot from the user are analyzed to filter out abusive language and/or harmful content.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p.ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/52 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel pour la prise en charge des services des réseaux sociaux
H04L 51/046 - Interopérabilité avec d'autres applications ou services réseau
Aspects of the present disclosure involve a system for an augmented reality (AR) try-on experience with lighting adjustment. The system accesses an image that depicts a real-world person. The system retrieves an AR fashion item. The system applies the image and the AR fashion item to a machine learning model to estimate a lighting adjustment for the AR fashion item based on lighting properties of the real-world person depicted in the image, the machine learning model trained to match lighting properties of images depicting real -world objects to lighting properties of AR objects and vice versa. The system combines the AR fashion item with the image that depicts the real-world person based on the estimated lighting adjustment to generate a modified image.
An Augmented Reality (AR) graphics system is provided. The AR graphics system may coordinate the display of augmented reality graphics created by multiple users located in an environment. The AR graphics system may determine an alignment object located in the environment that is designated as a common origin of a real-world coordinate system that is used to determine where to display AR graphics within the environment. Additionally, a prioritization scheme is implemented to resolve conflicts between overlapping input provided by different users in order to generate a single version of AR graphics.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/03 - Dispositions pour convertir sous forme codée la position ou le déplacement d'un élément
G06T 11/80 - Création ou modification d'une image dessinée ou peinte à la main en utilisant un dispositif manuel d'entrée, p.ex. une souris, un crayon lumineux, des touches de direction sur le clavier
G06V 20/20 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les scènes de réalité augmentée
H04L 65/403 - Dispositions pour la communication multipartite, p.ex. pour les conférences
G06F 3/0346 - Dispositifs de pointage déplacés ou positionnés par l'utilisateur; Leurs accessoires avec détection de l’orientation ou du mouvement libre du dispositif dans un espace en trois dimensions [3D], p.ex. souris 3D, dispositifs de pointage à six degrés de liberté [6-DOF] utilisant des capteurs gyroscopiques, accéléromètres ou d’inclinaiso
An eXtended Reality (XR) system provides methodologies for displaying virtual objects in a hand-centric XR experience. The XR system provides an XR user interface of an XR system to a user. The XR system captures video frame data of a hand of the user and detects the hand of the user based on the video frame data and a hand-detecting model. The XR system generates a cropping boundary box based on the detection of the hand and the video frame data and generates cropped video frame data based on the cropping boundary box and the video frame data. The XR system generates a 3D model of a portion of the hand of the user based on the cropped video frame data and a virtual object based on the 3D model of the portion of the hand of the user and a 3D texture. The XR displays the virtual object in the XR user interface.
A chatbot system for filtering conversation content. A chatbot system receives, from a client system, a prompt of a user during an interactive session. The chatbot system filters the prompt of the user based on a set of platform policies and generates a response based on the filtering of the prompt of the user, and communicates the response to the client system.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p.ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p.ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p.ex. messagerie instantanée [IM]
H04L 51/214 - Surveillance ou traitement des messages en utilisant le transfert sélectif
Embodiments herein describe a portal system for maintaining a user portal that is used for managing and generating scannable images. The scannable images may be linked to different types of content such as website URLs, social media platform user profiles, and augmented reality experiences. The user portal is accessible from a social media platform via user login credentials. Upon entering their user login credentials, the portal system authenticates the user login credentials and displays a list existing scannable images that are stored in association with the user portal. The user may modify one or more aspects of the existing scannable images or may generate a new scannable image from the user interface.
G06K 19/06 - Supports d'enregistrement pour utilisation avec des machines et avec au moins une partie prévue pour supporter des marques numériques caractérisés par le genre de marque numérique, p.ex. forme, nature, code
Aspects of the present disclosure involve a system for providing a browsing-based augmented reality (AR) try-on experience. The system displays a graphical user interface (GUI) comprising a set of fashion items. The system receives a real-time video feed from a camera of the device, the real-time video feed depicting a person. The system retrieves a first AR fashion item corresponding to a first fashion item in the set of fashion items included in the GUI. The system modifies the real-time video feed based on application of the first AR fashion item to the depiction of the person in the real-time video feed. The system presents the modified real-time video feed together with the GUI comprising the set of fashion items.
Aspects of the present disclosure involve a system for providing an AR try-on experience for a friend. The system accesses a plurality of images that depict one or more persons. The system receives input that identifies a given person of the one or more persons who is depicted in an individual image of the plurality of images. The system extracts features of the given person depicted in the individual image. The system applies a machine learning model to the extracted features of the given person to generate an avatar that resembles the given person. The system applies one or more augmented reality (AR) fashion items to the avatar to generate an image that resembles the given person wearing the one or more AR fashion items.
G06V 20/20 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les scènes de réalité augmentée
G06V 40/10 - Corps d’êtres humains ou d’animaux, p.ex. occupants de véhicules automobiles ou piétons; Parties du corps, p.ex. mains
G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
98.
RECOVERY IN A SYSTEM HAVING TWO SYSTEM-ON-CHIPS AND A MICROCONTROLLER AFTER OVER-THE-AIR UPDATE
A method performed on an augmented reality (AR) wearable device includes providing an over-the-air (OTA) update to all processors of a three-processor system, each processor of the three processor system having a respective current plurality of partitions and a respective last plurality of partitions. A communication error is detected between two or more processors of the three processor system after the OTA update. Respective active partitions are set for at least one of three processors to the last respective partition. The three processors are rebooted.
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p.ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
The subject technology receives an input image and a segmentation mask of the input image. The subject technology obtains reconstructed noise of the input image using the input image and the segmentation mask. The subject technology determines a first set of features by performing a first portion of a forward pass of the reconstructed noise through a decoder. The subject technology determines a second set of features by processing the input image for stable diffusion using an image to image (IMG2IMG) model. The subject technology generates a third set of features based on combining, using the segmentation mask, the first set of features and the second set of features with the reconstructed noise. The subject technology generates an output image by performing a remaining portion of the forward pass of the third set of features through the decoder.
Aspects of the present disclosure involve a system for providing a footwear mirror experience. The system accesses a video stream captured by a camera directed at feet of users. The system detects a depiction of one or more feet of a user in the video stream captured by the camera. The system, in response to detecting the one or more feet in the video stream captured by the camera, activates a display screen that is positioned at an eye-level of the user and presents the video stream including the depiction of the one or more feet of the user on the display screen.
brevets
