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    Image landmark detection
    11.
    发明授权
    Image landmark detection 有权

    公开(公告)号：US11354922B2

    公开(公告)日：2022-06-07

    申请号：US17138177

    申请日：2020-12-30

    Applicant: Snap Inc.

    Inventor： Sergey Tulyakov ,  Roman Furko ,  Aleksei Stoliar

    IPC: G06V40/10 ,  G06K9/62 ,  G06T7/246 ,  G06T7/73 ,  G06T11/60 ,  G06V20/40

    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.

    STEP DISTILLATION FOR LATENT DIFFUSION MODELS
    13.
    发明申请
    STEP DISTILLATION FOR LATENT DIFFUSION MODELS 有权

    公开(公告)号：US20240394843A1

    公开(公告)日：2024-11-28

    申请号：US18434411

    申请日：2024-02-06

    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: G06T5/60 ,  G06T5/70

    Abstract: 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.

    Compressing image-to-image models with average smoothing
    14.
    发明授权
    Compressing image-to-image models with average smoothing 有权

    公开(公告)号：US12154303B2

    公开(公告)日：2024-11-26

    申请号：US18238979

    申请日：2023-08-28

    Applicant: Snap Inc.

    Inventor： Jian Ren ,  Menglei Chai ,  Sergey Tulyakov ,  Qing Jin

    IPC: G06T9/00 ,  G06N3/08

    Abstract: System and methods for compressing image-to-image models. Generative Adversarial Networks (GANs) have achieved success in generating high-fidelity images. An image compression system and method adds a novel variant to class-dependent parameters (CLADE), referred to as CLADE-Avg, which recovers the image quality without introducing extra computational cost. An extra layer of average smoothing is performed between the parameter and normalization layers. Compared to CLADE, this image compression system and method smooths abrupt boundaries, and introduces more possible values for the scaling and shift. In addition, the kernel size for the average smoothing can be selected as a hyperparameter, such as a 3×3 kernel size. This method does not introduce extra multiplications but only addition, and thus does not introduce much computational overhead, as the division can be absorbed into the parameters after training.

    Image face manipulation
    18.
    发明授权
    Image face manipulation 有权

    公开(公告)号：US11798261B2

    公开(公告)日：2023-10-24

    申请号：US17303871

    申请日：2021-06-09

    Applicant: Snap Inc.

    Inventor： Chen Cao ,  Sergey Tulyakov ,  Zhenglin Geng

    IPC: G06V40/16 ,  G06V10/82 ,  G06V10/764

    CPC classification number: G06V10/764 ,  G06V10/82 ,  G06V40/161 ,  G06V40/168 ,  G06V40/172 ,  G06V40/175

    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.

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