Abstract: Various implementations disclosed herein include devices, systems, and methods that generates values for a representation of a face of a user. For example, an example process may include obtaining sensor data (e.g., live data) of a user, wherein the sensor data is associated with a point in time, generating a set of values representing the user based on the sensor data, and providing the set of values, where a depiction of the user at the point in time is displayed based on the set of values. In some implementations, the set of values includes depth values that define three-dimensional (3D) positions of portions of the user relative to multiple 3D positions of points of a projected surface and appearance values (e.g., color, texture, opacity, etc.) that define appearances of the portions of the user.

Abstract: Advances in deep generative models (DGM) have led to the development of neural face video compression codecs that are capable of using an order of magnitude less data than “traditional” engineered codecs. These “neural” codecs can reconstruct a target image by warping a source image to approximate the content of the target image and using a DGM to compensate for imperfections in the warped source image. The determined warping operation may be encoded and transmitted using less data (e.g., transmitting a small number of keypoints, rather than a dense flow field), leading to the bandwidth savings compared to traditional codecs. However, by relying on a single source image only, these methods can lead to inaccurate reconstructions. The techniques presented herein improve image reconstruction quality while maintaining bandwidth savings, via a combination of using multiple source images (i.e., containing multiple views of the first human subject) and novel feature aggregation techniques.

Abstract: Various implementations disclosed herein include devices, systems, and methods that generates and displays a portion of a representation of a face of a user. For example, an example process may include obtaining a first set of data corresponding to features of a face of a user in a plurality of configurations, while a user is using an electronic device, obtaining a second set of data corresponding to one or more partial views of the face from one or more image sensors, generating a representation of the face of the user based on the first set of data and the second set of data, wherein portions of the representation correspond to different confidence values, and displaying the portions of the representation based on the corresponding confidence values.

Abstract: Various implementations disclosed herein include devices, systems, and methods that use event camera data to track deformable objects such as faces, hands, and other body parts. One exemplary implementation involves receiving a stream of pixel events output by an event camera. The device tracks the deformable object using this data. Various implementations do so by generating a dynamic representation of the object and modifying the dynamic representation of the object in response to obtaining additional pixel events output by the event camera. In some implementations, generating the dynamic representation of the object involves identifying features disposed on the deformable surface of the object using the stream of pixel events. The features are determined by identifying patterns of pixel events. As new event stream data is received, the patterns of pixel events are recognized in the new data and used to modify the dynamic representation of the object.

Abstract: Systems and methods for generating a video of an emoji that has been puppeted using inputs from image, depth, and audio. The inputs can capture facial expressions of a user, eye, eyebrow, mouth, and head movements. A pose, held by the user, can be detected that can be used to generate supplemental animation. The emoji can further be animated using physical properties associated with the emoji and captured movements. An emoji of a dog can have its ears move in response to an up-and-down movement, or a shaking of the head. The video can be sent in a message to one or more recipients. A sending device can render the puppeted video in accordance with hardware and software capabilities of a recipient's computer device.

Abstract: A three-dimensional model (e.g., motion capture model) of a user is generated from captured images or captured video of the user. A machine learning network may track poses and expressions of the user to generate and refine the three-dimensional model. Refinement of the three-dimensional model may provide more accurate tracking of the user's face. Refining of the three-dimensional model may include refining the determinations of poses and expressions at defined locations (e.g., eye corners and/or nose) in the three-dimensional model. The refining may occur in an iterative process. Tracking of the three-dimensional model over time (e.g., during video capture) may be used to generate an animated three-dimensional model (e.g., an animated puppet) of the user that simulates the user's poses and expressions.

Abstract: Various implementations disclosed herein include devices, systems, and methods that use event camera data to track deformable objects such as faces, hands, and other body parts. One exemplary implementation involves receiving a stream of pixel events output by an event camera. The device tracks the deformable object using this data. Various implementations do so by generating a dynamic representation of the object and modifying the dynamic representation of the object in response to obtaining additional pixel events output by the event camera. In some implementations, generating the dynamic representation of the object involves identifying features disposed on the deformable surface of the object using the stream of pixel events. The features are determined by identifying patterns of pixel events. As new event stream data is received, the patterns of pixel events are recognized in the new data and used to modify the dynamic representation of the object.

Abstract: A three-dimensional model (e.g., motion capture model) of a user is generated from captured images or captured video of the user. A machine learning network may track poses and expressions of the user to generate and refine the three-dimensional model. Refinement of the three-dimensional model may provide more accurate tracking of the user's face. Refining of the three-dimensional model may include refining the determinations of poses and expressions at defined locations (e.g., eye corners and/or nose) in the three-dimensional model. The refining may occur in an iterative process. Tracking of the three-dimensional model over time (e.g., during video capture) may be used to generate an animated three-dimensional model (e.g., an animated puppet) of the user that simulates the user's poses and expressions.

Abstract: A three-dimensional model (e.g., motion capture model) of a user is generated from captured images or captured video of the user. A machine learning network may track poses and expressions of the user to generate and refine the three-dimensional model. Refinement of the three-dimensional model may provide more accurate tracking of the user's face. Refining of the three-dimensional model may include refining the determinations of poses and expressions at defined locations (e.g., eye corners and/or nose) in the three-dimensional model. The refining may occur in an iterative process. Tracking of the three-dimensional model over time (e.g., during video capture) may be used to generate an animated three-dimensional model (e.g., an animated puppet) of the user that simulates the user's poses and expressions.

Abstract: Systems and methods for generating a video of an emoji that has been puppeted using inputs from image, depth, and audio. The inputs can capture facial expressions of a user, eye, eyebrow, mouth, and head movements. A pose, held by the user, can be detected that can be used to generate supplemental animation. The emoji can further be animated using physical properties associated with the emoji and captured movements. An emoji of a dog can have its ears move in response to an up-and-down movement, or a shaking of the head. The video can be sent in a message to one or more recipients. A sending device can render the puppeted video in accordance with hardware and software capabilities of a recipient's computer device.

Abstract: A three-dimensional model (e.g., motion capture model) of a user is generated from captured images or captured video of the user. A machine learning network may track poses and expressions of the user to generate and refine the three-dimensional model. Refinement of the three-dimensional model may provide more accurate tracking of the user's face. Refining of the three-dimensional model may include refining the determinations of poses and expressions at defined locations (e.g., eye corners and/or nose) in the three-dimensional model. The refining may occur in an iterative process. Tracking of the three-dimensional model over time (e.g., during video capture) may be used to generate an animated three-dimensional model (e.g., an animated puppet) of the user that simulates the user's poses and expressions.

Abstract: Systems and methods for generating a video of an emoji that has been puppeted using inputs from image, depth, and audio. The inputs can capture facial expressions of a user, eye, eyebrow, mouth, and head movements. A pose, held by the user, can be detected that can be used to generate supplemental animation. The emoji can further be animated using physical properties associated with the emoji and captured movements. An emoji of a dog can have its ears move in response to an up-and-down movement, or a shaking of the head. The video can be sent in a message to one or more recipients. A sending device can render the puppeted video in accordance with hardware and software capabilities of a recipient's computer device.

Abstract: Systems and methods for generating a video of an emoji that has been puppeted using inputs from image, depth, and audio. The inputs can capture facial expressions of a user, eye, eyebrow, mouth, and head movements. A pose, held by the user, can be detected that can be used to generate supplemental animation. The emoji can further be animated using physical properties associated with the emoji and captured movements. An emoji of a dog can have its ears move in response to an up-and-down movement, or a shaking of the head. The video can be sent in a message to one or more recipients. A sending device can render the puppeted video in accordance with hardware and software capabilities of a recipient's computer device.

Abstract: In an embodiment a method of online video communication is disclosed. An online video communication is established between a source device and a receiving device. The source device captures a live video recording of a sending user. The captured recording is analyzed to identify one or more characteristics of the sending user. The source device then generates avatar data corresponding to the identified characteristics. The avatar data is categorized into a plurality of groups, wherein a first group of the at least two groups comprises avatar data that is more unique to the sending user. Finally, at least the first group of the plurality of groups is transmitted to the receiving device. The transmitted first group of avatar data defines, at least in part, how to animate an avatar that mimics the sending user's one or more physical characteristics.