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    Wire segmentation for images using machine learning
    2.
    发明授权
    Wire segmentation for images using machine learning 有权

    公开(公告)号：US12271804B2

    公开(公告)日：2025-04-08

    申请号：US17870496

    申请日：2022-07-21

    Applicant: Adobe Inc.

    Inventor： Mang Tik Chiu ,  Connelly Barnes ,  Zijun Wei ,  Zhe Lin ,  Yuqian Zhou ,  Xuaner Zhang ,  Sohrab Amirghodsi ,  Florian Kainz ,  Elya Shechtman

    IPC: G06N3/045 ,  G06T3/40 ,  G06T5/30 ,  G06T5/77 ,  G06T7/11 ,  G06T7/143 ,  G06T7/187 ,  G06T7/62

    Abstract: Embodiments are disclosed for performing wire segmentation of images using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input image, generating, by a first trained neural network model, a global probability map representation of the input image indicating a probability value of each pixel including a representation of wires, and identifying regions of the input image indicated as including the representation of wires. The disclosed systems and methods further comprise, for each region from the identified regions, concatenating the region and information from the global probability map to create a concatenated input, and generating, by a second trained neural network model, a local probability map representation of the region based on the concatenated input, indicating pixels of the region including representations of wires. The disclosed systems and methods further comprise aggregating local probability maps for each region.

    Generating shadows for digital objects within digital images utilizing a height map
    3.
    发明授权
    Generating shadows for digital objects within digital images utilizing a height map 有权

    公开(公告)号：US12169895B2

    公开(公告)日：2024-12-17

    申请号：US17502782

    申请日：2021-10-15

    Applicant: Adobe Inc.

    Inventor： Yifan Liu ,  Jianming Zhang ,  He Zhang ,  Elya Shechtman ,  Zhe Lin

    IPC: G06T15/60 ,  G06N3/045 ,  G06T7/194 ,  G06T7/70 ,  G06T11/00

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate a height map for a digital object portrayed in a digital image and further utilizes the height map to generate a shadow for the digital object. Indeed, in one or more embodiments, the disclosed systems generate (e.g., utilizing a neural network) a height map that indicates the pixels heights for pixels of a digital object portrayed in a digital image. The disclosed systems utilize the pixel heights, along with lighting information for the digital image, to determine how the pixels of the digital image project to create a shadow for the digital object. Further, in some implementations, the disclosed systems utilize the determined shadow projections to generate (e.g., utilizing another neural network) a soft shadow for the digital object. Accordingly, in some cases, the disclosed systems modify the digital image to include the shadow.

    PANOPTICALLY GUIDED INPAINTING UTILIZING A PANOPTIC INPAINTING NEURAL NETWORK
    8.
    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.

    Generating synthesized digital images utilizing class-specific machine-learning models
    10.
    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

    Applicant: Adobe Inc.

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

    IPC: G06T11/00

    CPC classification number: G06T11/00 ,  G06T2210/12

    Abstract: 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.

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