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    FLOW-GUIDED MOTION RETARGETING
    51.
    发明申请
    FLOW-GUIDED MOTION RETARGETING 有权

    公开(公告)号：US20220207786A1

    公开(公告)日：2022-06-30

    申请号：US17557834

    申请日：2021-12-21

    Applicant: Snap Inc.

    Inventor： Jian Ren ,  Menglei Chai ,  Oliver Woodford ,  Kyle Olszewski ,  Sergey Tulyakov

    IPC: G06T11/00 ,  G06V40/10 ,  G06T7/194 ,  G06T3/60 ,  G06N3/04

    Abstract: Systems and methods herein describe a motion retargeting system. The motion retargeting system accesses a plurality of two-dimensional images comprising a person performing a plurality of body poses, extracts a plurality of implicit volumetric representations from the plurality of body poses, generates a three-dimensional warping field, the three-dimensional warping field configured to warp the plurality of implicit volumetric representations from a canonical pose to a target pose, and based on the three-dimensional warping field, generates a two-dimensional image of an artificial person performing the target pose.

    LOSS DETERMINATION FOR LATENT DIFFUSION MODELS
    54.
    发明申请
    LOSS DETERMINATION FOR LATENT DIFFUSION MODELS 有权

    公开(公告)号：US20240394933A1

    公开(公告)日：2024-11-28

    申请号：US18596452

    申请日：2024-03-05

    Applicant: Snap Inc.

    Inventor： Pavlo Chemerys ,  Colin Eles ,  Ju Hu ,  Qing Jin ,  Yanyu Li ,  Ergeta Muca ,  Jian Ren ,  Dhritiman Sagar ,  Aleksei Stoliar ,  Sergey Tulyakov ,  Huan Wang

    IPC: G06T11/00

    Abstract: 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.

    TEXT-GUIDED CAMEO GENERATION
    58.
    发明公开
    TEXT-GUIDED CAMEO GENERATION 审中-公开

    公开(公告)号：US20240104789A1

    公开(公告)日：2024-03-28

    申请号：US17950945

    申请日：2022-09-22

    Applicant: Snap Inc.

    Inventor： Arnab Ghosh ,  Jian Ren ,  Pavel Savchenkov ,  Sergey Tulyakov

    IPC: G06T11/00 ,  G06F40/289 ,  G06F40/35 ,  G06V40/16

    CPC classification number: G06T11/00 ,  G06F40/289 ,  G06F40/35 ,  G06V40/161

    Abstract: A method of generating an image for use in a conversation taking place in a messaging application is disclosed. Conversation input text is received from a user of a portable device that includes a display. Model input text is generated from the conversation input text, which is processed with a text-to-image model to generate an image based on the model input text. The coordinates of a face in the image are determined, and the face of the user or another person is added to the image at the location. The final image is displayed on the portable device, and user input is received to transmit the image to a remote recipient.

    Motion representations for articulated animation
    60.
    发明授权
    Motion representations for articulated animation 有权

    公开(公告)号：US11836835B2

    公开(公告)日：2023-12-05

    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.

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