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    AUTOMATED AUGMENTED REALITY EXPERIENCE CREATION SYSTEM
    3.
    发明公开
    AUTOMATED AUGMENTED REALITY EXPERIENCE CREATION SYSTEM 审中-公开

    公开(公告)号：US20230386144A1

    公开(公告)日：2023-11-30

    申请号：US17804500

    申请日：2022-05-27

    Applicant: Snap Inc.

    Inventor： Konstantin Gudkov ,  Andrei Zharkov ,  Vadim Velicodnii ,  Aleksei Zhuravlev ,  Sergey Demyanov

    IPC: G06T19/00 ,  G06F3/01 ,  G06F3/04815 ,  G06F8/34

    CPC classification number: G06T19/006 ,  G06F3/011 ,  G06F3/04815 ,  G06F8/34

    Abstract: Methods and systems are disclosed for performing automatically creating AR experiences on a messaging platform. The methods and systems perform operations that include: receiving, via a graphical user interface (GUI), input that specifies a plurality of image transformation parameters; accessing a set of sample source images; modifying the set of sample source images based on the plurality of image transformation parameters to generate a set of sample target images; training a machine learning model to generate a given target image from a given source image by establishing a relationship between the set of sample source images and the set of sample target images; and automatically generating an augmented reality experience comprising the trained machine learning model.

    PROTECTING IMAGE FEATURES IN STYLIZED REPRESENTATIONS OF A SOURCE IMAGE
    4.
    发明公开
    PROTECTING IMAGE FEATURES IN STYLIZED REPRESENTATIONS OF A SOURCE IMAGE 审中-公开

    公开(公告)号：US20230215062A1

    公开(公告)日：2023-07-06

    申请号：US17804268

    申请日：2022-05-26

    Applicant: Snap Inc.

    Inventor： Konstantin Gudkov ,  Sergey Demyanov ,  Andrei Zharkov ,  Fedor Zhdanov ,  Vadim Velicodnii

    IPC: G06T11/60 ,  G06N20/20

    CPC classification number: G06T11/60 ,  G06N20/20 ,  G06T2210/44

    Abstract: Systems and methods herein describe an image stylization system. The image stylization system accesses a set of images corresponding to a target domain style, generates a set of paired images using a first machine learning model, analyze the generated set of paired images using a second machine learning model trained to analyze the generated set of paired images based on a plurality of protected feature criteria, determines a set of image transformations for the generated set of pairs, generates a transformed set of paired images by performing the set of image transformations on the set of paired images, and generates stylized images corresponding to the target domain style using a supervised image translation model trained on the transformed set of paired images.

    Domain changes in generative adversarial networks
    6.
    发明授权
    Domain changes in generative adversarial networks 有权

    公开(公告)号：US12148202B2

    公开(公告)日：2024-11-19

    申请号：US17841333

    申请日：2022-06-15

    Applicant: Snap Inc.

    Inventor： Sergey Demyanov ,  Konstantin Gudkov ,  Fedor Zhdanov ,  Andrei Zharkov

    IPC: G06T11/00 ,  G06T3/18 ,  G06T7/68 ,  G06V10/74 ,  G06V10/774 ,  G06V10/82 ,  G06V40/16

    Abstract: An image manipulation system for generating modified images using a generative adversarial network (GAN) trains GANs using domain changes, aligns input images with generated images, classifies and associates target images based on a symmetry, and uses a modified discriminator structure. A method for domain changes includes generating, using a pre-trained GAN trained on a plurality of first target images, a plurality of images, and determining a feature for each of the plurality of images. The method further includes determining the feature for each of a plurality of second target images and matching, based on the feature, second target images of the plurality of second target images with the plurality of images. The method further includes training a discriminator of the pre-trained GAN with the second target images and the plurality of images.

    DOMAIN CHANGES IN GENERATIVE ADVERSARIAL NETWORKS
    7.
    发明申请
    DOMAIN CHANGES IN GENERATIVE ADVERSARIAL NETWORKS 有权

    公开(公告)号：US20250037436A1

    公开(公告)日：2025-01-30

    申请号：US18911590

    申请日：2024-10-10

    Applicant: Snap Inc.

    Inventor： Sergey Demyanov ,  Konstantin Gudkov ,  Fedor Zhdanov ,  Andrei Zharkov

    IPC: G06V10/774 ,  G06T3/18 ,  G06T7/68 ,  G06T11/00 ,  G06V10/74 ,  G06V10/82 ,  G06V40/16

    Abstract: An image manipulation system for generating modified images using a generative adversarial network (GAN) trains GANs using domain changes, aligns input images with generated images, classifies and associates target images based on a symmetry, and uses a modified discriminator structure. A method for domain changes includes generating, using a pre-trained GAN trained on a plurality of first target images, a plurality of images, and determining a feature for each of the plurality of images. The method further includes determining the feature for each of a plurality of second target images and matching, based on the feature, second target images of the plurality of second target images with the plurality of images. The method further includes training a discriminator of the pre-trained GAN with the second target images and the plurality of images.

    GENERATIVE ADVERSARIAL NETWORK MANIPULATED IMAGE EFFECTS
    9.
    发明申请
    GENERATIVE ADVERSARIAL NETWORK MANIPULATED IMAGE EFFECTS 有权

    公开(公告)号：US20220207355A1

    公开(公告)日：2022-06-30

    申请号：US17318658

    申请日：2021-05-12

    Applicant: Snap Inc.

    Inventor： Sergey Demyanov ,  Konstantin Gudkov ,  Aleksei Stoliar ,  Roman Ushakov ,  Fedor Zhdanov

    IPC: G06N3/08 ,  G06N3/04 ,  G06T11/60

    Abstract: Systems and methods herein describe an image manipulation system for generating modified images using a generative adversarial network. The image manipulation system accesses a pre-trained generative adversarial network (GAN), fine-tunes the pre-trained GAN by training a portion of existing neural network layers of the pre-trained GAN and newly added layers of the pre-trained GAN on a secondary image domain, adjusts the weights of the fine-tuned GAN using the weights of the pre-trained GAN, and stores the fine-tuned GAN. An image transformation system uses the generated modified images to train a subsequent neural network, which can access a face from a client device and transform it to a domain of images used for GAN fine-tuning.

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