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    PANOPTICALLY GUIDED INPAINTING UTILIZING A PANOPTIC INPAINTING NEURAL NETWORK
    5.
    发明公开
    PANOPTICALLY GUIDED INPAINTING UTILIZING A PANOPTIC INPAINTING NEURAL NETWORK 审中-公开

    公开(公告)号：US20240127410A1

    公开(公告)日：2024-04-18

    申请号：US17937695

    申请日：2022-10-03

    Applicant: Adobe Inc.

    Inventor： Zhe Lin ,  Haitian Zheng ,  Elya Shechtman ,  Jianming Zhang ,  Jingwan Lu ,  Ning Xu ,  Qing Liu ,  Scott Cohen ,  Sohrab Amirghodsi

    IPC: G06T5/00 ,  G06T7/11

    CPC classification number: G06T5/005 ,  G06T7/11 ,  G06T2207/20084

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

    OBJECT CLASS INPAINTING IN DIGITAL IMAGES UTILIZING CLASS-SPECIFIC INPAINTING NEURAL NETWORKS
    8.
    发明公开
    OBJECT CLASS INPAINTING IN DIGITAL IMAGES UTILIZING CLASS-SPECIFIC INPAINTING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20230368339A1

    公开(公告)日：2023-11-16

    申请号：US17663317

    申请日：2022-05-13

    Applicant: Adobe Inc.

    Inventor： Haitian Zheng ,  Zhe Lin ,  Jingwan Lu ,  Scott Cohen ,  Elya Shechtman ,  Connelly Barnes ,  Jianming Zhang ,  Ning Xu ,  Sohrab Amirghodsi

    IPC: G06T5/00 ,  G06T7/11 ,  G06N3/04

    CPC classification number: G06T5/005 ,  G06T7/11 ,  G06N3/04 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing class-specific cascaded modulation inpainting neural network. For example, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network that includes cascaded modulation decoder layers to generate replacement pixels portraying a particular target object class. To illustrate, in response to user selection of a replacement region and target object class, the disclosed systems utilize a class-specific cascaded modulation inpainting neural network corresponding to the target object class to generate an inpainted digital image that portrays an instance of the target object class within the replacement region. Moreover, in one or more embodiments the disclosed systems train class-specific cascaded modulation inpainting neural networks corresponding to a variety of target object classes, such as a sky object class, a water object class, a ground object class, or a human object class.

    Labeling techniques for a modified panoptic labeling neural network
    9.
    发明授权
    Labeling techniques for a modified panoptic labeling neural network 有权

    公开(公告)号：US11507777B2

    公开(公告)日：2022-11-22

    申请号：US15930539

    申请日：2020-05-13

    Applicant: Adobe Inc.

    Inventor： Sohrab Amirghodsi ,  Zhe Lin ,  Yilin Wang ,  Tianshu Yu ,  Connelly Barnes ,  Elya Shechtman

    IPC: G06K9/62 ,  G06V10/75 ,  G06F17/18 ,  G06N3/08 ,  G06N20/00

    Abstract: A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.

    PERFORMING PATCH MATCHING GUIDED BY A TRANSFORMATION GAUSSIAN MIXTURE MODEL
    10.
    发明申请
    PERFORMING PATCH MATCHING GUIDED BY A TRANSFORMATION GAUSSIAN MIXTURE MODEL 有权

    公开(公告)号：US20210319256A1

    公开(公告)日：2021-10-14

    申请号：US17332773

    申请日：2021-05-27

    Applicant: Adobe Inc.

    Inventor： Xin Sun ,  Sohrab Amirghodsi ,  Nathan Carr ,  Michal Lukac

    IPC: G06K9/62 ,  G06T11/60

    Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating a modified digital image by identifying patch matches within a digital image utilizing a Gaussian mixture model. For example, the systems described herein can identify sample patches and corresponding matching portions within a digital image. The systems can also identify transformations between the sample patches and the corresponding matching portions. Based on the transformations, the systems can generate a Gaussian mixture model, and the systems can modify a digital image by replacing a target region with target matching portions identified in accordance with the Gaussian mixture model.

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