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    Image editing by a generative adversarial network using keypoints or segmentation masks constraints
    6.
    发明授权
    Image editing by a generative adversarial network using keypoints or segmentation masks constraints 有权

    公开(公告)号：US11157773B2

    公开(公告)日：2021-10-26

    申请号：US16802243

    申请日：2020-02-26

    申请人： ADOBE INC.

    发明人： Cameron Smith ,  Yannick Hold-Geoffroy ,  Mariia Drozdova

    IPC分类号： G06K9/62 ,  G06K9/46 ,  G06K9/00

    摘要： Images can be edited to include features similar to a different target image. An unconditional generative adversarial network (GAN) is employed to edit features of an initial image based on a constraint determined from a target image. The constraint used by the GAN is determined from keypoints or segmentation masks of the target image, and edits are made to features of the initial image based on keypoints or segmentation masks of the initial image corresponding to those of the constraint from the target image. The GAN modifies the initial image based on a loss function having a variable for the constraint. The result of this optimization process is a modified initial image having features similar to the target image subject to the constraint determined from the identified keypoints or segmentation masks.

    AUTOMATIC IMAGE WARPING FOR WARPED IMAGE GENERATION
    7.
    发明申请
    AUTOMATIC IMAGE WARPING FOR WARPED IMAGE GENERATION 有权

    公开(公告)号：US20210319532A1

    公开(公告)日：2021-10-14

    申请号：US16848741

    申请日：2020-04-14

    申请人： Adobe Inc.

    发明人： Julia Gong ,  Yannick Hold-Geoffroy ,  Jingwan Lu

    IPC分类号： G06T3/00 ,  G06T7/00 ,  G06T11/60

    摘要： Techniques and systems are provided for configuring neural networks to perform warping of an object represented in an image to create a caricature of the object. For instance, in response to obtaining an image of an object, a warped image generator generates a warping field using the image as input. The warping field is generated using a model trained with pairings of training images and known warped images using supervised learning techniques and one or more losses. The warped image generator determines, based on the warping field, a set of displacements associated with pixels of the input image. These displacements indicate pixel displacement directions for the pixels of the input image. These displacements are applied to the digital image to generate a warped image of the object.

    ANONYMIZING DIGITAL IMAGES UTILIZING A GENERATIVE ADVERSARIAL NEURAL NETWORK
    10.
    发明公开
    ANONYMIZING DIGITAL IMAGES UTILIZING A GENERATIVE ADVERSARIAL NEURAL NETWORK 审中-公开

    公开(公告)号：US20240143835A1

    公开(公告)日：2024-05-02

    申请号：US18052121

    申请日：2022-11-02

    申请人： Adobe Inc.

    发明人： Siavash Khodadadeh ,  Ratheesh Kalarot ,  Shabnam Ghadar ,  Yannick Hold-Geoffroy

    IPC分类号： G06F21/62 ,  G06N3/0455 ,  G06N3/0475

    CPC分类号： G06F21/6254 ,  G06N3/0455 ,  G06N3/0475

    摘要： The present disclosure relates to systems, methods, and non-transitory computer readable media for generating anonymized digital images utilizing a face anonymization neural network. In some embodiments, the disclosed systems utilize a face anonymization neural network to extract or encode a face anonymization guide that encodes face attribute features, such as gender, ethnicity, age, and expression. In some cases, the disclosed systems utilize the face anonymization guide to inform the face anonymization neural network in generating synthetic face pixels for anonymizing a digital image while retaining attributes, such as gender, ethnicity, age, and expression. The disclosed systems learn parameters for a face anonymization neural network for preserving face attributes, accounting for multiple faces in digital images, and generating synthetic face pixels for faces in profile poses.