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    PLOTTING BEHIND THE SCENES WITH LEARNABLE GAME ENGINES
    31.
    发明公开
    PLOTTING BEHIND THE SCENES WITH LEARNABLE GAME ENGINES 审中-公开

    公开(公告)号：US20240307783A1

    公开(公告)日：2024-09-19

    申请号：US18121268

    申请日：2023-03-14

    Applicant: Snap Inc.

    Inventor： Willi Menapace ,  Aliaksandr Siarohin ,  Sergey Tulyakov

    IPC: A63F13/67 ,  A63F13/57

    CPC classification number: A63F13/67 ,  A63F13/57

    Abstract: A framework trains game-engine-like neural models from annotated videos to generate a Learnable Game Engine (LGE) that maintains states of the scene, objects and agents in it, and enables rendering the environment from a controllable viewpoint. The LGE models the logic of the game and the rules of physics, making it possible for the user to play the game by specifying both high- and low-level action sequences. The LGE also unlocks a director's mode where the game is played by plotting behind the scenes, specifying high-level actions and goals for the agents using text-based instructions. To implement the director's mode, a trained diffusion-based animation model navigates the scene using high-level constraints, to enable play against an adversary, and to devise the strategy to win a point. To render the resulting state of the environment and its agents, a compositional neural radiance field (NeRF) representation is used in a synthesis model.

    Motion representations for articulated animation
    35.
    发明授权
    Motion representations for articulated animation 有权

    公开(公告)号：US11798213B2

    公开(公告)日：2023-10-24

    申请号：US17364218

    申请日：2021-06-30

    Applicant: Snap Inc.

    Inventor： Menglei Chai ,  Jian Ren ,  Aliaksandr Siarohin ,  Sergey Tulyakov ,  Oliver Woodford

    IPC: G06T13/00 ,  G06N3/08 ,  G06T7/20 ,  G06T11/00 ,  G06N3/045

    CPC classification number: G06T13/00 ,  G06N3/045 ,  G06N3/08 ,  G06T7/20 ,  G06T11/001 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: Systems and methods herein describe novel motion representations for animating articulated objects consisting of distinct parts. The described systems and method access source image data, identify driving image data to modify image feature data in the source image sequence data, generate, using an image transformation neural network, modified source image data comprising a plurality of modified source images depicting modified versions of the image feature data, the image transformation neural network being trained to identify, for each image in the source image data, a driving image from the driving image data, the identified driving image being implemented by the image transformation neural network to modify a corresponding source image in the source image data using motion estimation differences between the identified driving image and the corresponding source image, and stores the modified source image data.

    Cloud based machine learning
    38.
    发明授权
    Cloud based machine learning 有权

    公开(公告)号：US11334815B1

    公开(公告)日：2022-05-17

    申请号：US16147105

    申请日：2018-09-28

    Applicant: Snap Inc.

    Inventor： Eric Buehl ,  Jordan Hurwitz ,  Sergey Tulyakov ,  Shubham Vij

    IPC: G06N20/00 ,  H04L29/08 ,  H04L67/10

    Abstract: Disclosed are various embodiments for implementing computational tasks in a cloud environment in one or more operating system level virtualized containers. A parameter file can specify different parameters including hardware parameters, library parameters, user code parameters, and job parameters (e.g., sets of hyperparameters). The parameter file can be converted via a mapping and implemented in a cloud-based container platform.

    IMAGE FACE MANIPULATION
    39.
    发明申请
    IMAGE FACE MANIPULATION 有权

    公开(公告)号：US20210295020A1

    公开(公告)日：2021-09-23

    申请号：US17303871

    申请日：2021-06-09

    Applicant: Snap Inc.

    Inventor： Chen Cao ,  Sergey Tulyakov ,  Zhenglin Geng

    IPC: G06K9/00

    Abstract: Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for synthesizing a realistic image with a new expression of a face in an input image by receiving an input image comprising a face having a first expression; obtaining a target expression for the face; and extracting a texture of the face and a shape of the face. The program and method for generating, based on the extracted texture of the face, a target texture corresponding to the obtained target expression using a first machine learning technique; generating, based on the extracted shape of the face, a target shape corresponding to the obtained target expression using a second machine learning technique; and combining the generated target texture and generated target shape into an output image comprising the face having a second expression corresponding to the obtained target expression.

    Image landmark detection
    40.
    发明授权
    Image landmark detection 有权

    公开(公告)号：US10909357B1

    公开(公告)日：2021-02-02

    申请号：US16277710

    申请日：2019-02-15

    Applicant: Snap Inc.

    Inventor： Sergey Tulyakov ,  Roman Furko ,  Aleksei Stoliar

    IPC: G06K9/00 ,  G06K9/62 ,  G06T7/246 ,  G06T7/73 ,  G06T11/60

    Abstract: A landmark detection system can more accurately detect landmarks in images using a detection scheme that penalizes for dispersion parameters, such as variance or scale. The landmark detection system can be trained using both labeled and unlabeled training data in a semi-supervised approach. The landmark detection system can further implement tracking of an object across multiple images using landmark data.

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