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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

    申请人： Adobe Inc.

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

    IPC分类号： G06T5/00 ,  G06T7/11

    CPC分类号： G06T5/005 ,  G06T7/11 ,  G06T2207/20084

    摘要： 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.

    Generating synthesized digital images utilizing class-specific machine-learning models
    7.
    发明授权
    Generating synthesized digital images utilizing class-specific machine-learning models 有权

    公开(公告)号：US11861762B2

    公开(公告)日：2024-01-02

    申请号：US17400474

    申请日：2021-08-12

    申请人： Adobe Inc.

    发明人： Yuheng Li ,  Yijun Li ,  Jingwan Lu ,  Elya Shechtman ,  Krishna Kumar Singh

    IPC分类号： G06T11/00

    CPC分类号： G06T11/00 ,  G06T2210/12

    摘要： This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images using class-specific generators for objects of different classes. The disclosed system modifies a synthesized digital image by utilizing a plurality of class-specific generator neural networks to generate a plurality of synthesized objects according to object classes identified in the synthesized digital image. The disclosed system determines object classes in the synthesized digital image such as via a semantic label map corresponding to the synthesized digital image. The disclosed system selects class-specific generator neural networks corresponding to the classes of objects in the synthesized digital image. The disclosed system also generates a plurality of synthesized objects utilizing the class-specific generator neural networks based on contextual data associated with the identified objects. The disclosed system generates a modified synthesized digital image by replacing the identified objects in the synthesized digital images with the synthesized objects.

    OBJECT CLASS INPAINTING IN DIGITAL IMAGES UTILIZING CLASS-SPECIFIC INPAINTING NEURAL NETWORKS
    9.
    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

    申请人： Adobe Inc.

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

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

    CPC分类号： G06T5/005 ,  G06T7/11 ,  G06N3/04 ,  G06T2207/20081 ,  G06T2207/20084

    摘要： 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.

    Generative image congealing
    10.
    发明授权
    Generative image congealing 有权

    公开(公告)号：US11762951B2

    公开(公告)日：2023-09-19

    申请号：US16951782

    申请日：2020-11-18

    申请人： Adobe Inc.

    发明人： Elya Shechtman ,  William Peebles ,  Richard Zhang ,  Jun-Yan Zhu ,  Alyosha Efros

    IPC分类号： G06F18/21 ,  G06N3/08 ,  G06T3/00 ,  G06F18/214 ,  G06N3/045

    CPC分类号： G06F18/217 ,  G06F18/214 ,  G06N3/045 ,  G06N3/08 ,  G06T3/0068

    摘要： Embodiments are disclosed for generative image congealing which provides an unsupervised learning technique that learns transformations of real data to improve the image quality of GANs trained using that image data. In particular, in one or more embodiments, the disclosed systems and methods comprise generating, by a spatial transformer network, an aligned real image for a real image from an unaligned real dataset, providing, by the spatial transformer network, the aligned real image to an adversarial discrimination network to determine if the aligned real image resembles aligned synthetic images generated by a generator network, and training, by a training manager, the spatial transformer network to learn updated transformations based on the determination of the adversarial discrimination network.