USER REPRESENTATION USING DEPTHS RELATIVE TO MULTIPLE SURFACE POINTS

    公开(公告)号:US20240005537A1

    公开(公告)日:2024-01-04

    申请号:US18214604

    申请日:2023-06-27

    Applicant: Apple Inc.

    CPC classification number: G06T7/521 G06V40/176 G06T13/40

    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.

    Neural face video compression using multiple views

    公开(公告)号:US11856203B1

    公开(公告)日:2023-12-26

    申请号:US17701498

    申请日:2022-03-22

    Applicant: Apple Inc.

    CPC classification number: H04N19/139 H04N19/132 H04N19/42

    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.

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