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    OBJECT CLASS INPAINTING IN DIGITAL IMAGES UTILIZING CLASS-SPECIFIC INPAINTING NEURAL NETWORKS
    13.
    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
    14.
    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.

    TEXTURE INTERPOLATION USING NEURAL NETWORKS
    15.
    发明申请
    TEXTURE INTERPOLATION USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200342634A1

    公开(公告)日：2020-10-29

    申请号：US16392968

    申请日：2019-04-24

    Applicant: Adobe Inc.

    Inventor： Connelly Barnes ,  Sohrab Amirghodsi ,  Michal Lukac ,  Elya Shechtman ,  Ning Yu

    IPC: G06T11/00 ,  G06T7/40

    Abstract: Techniques are disclosed for neural network based interpolation of image textures. A methodology implementing the techniques according to an embodiment includes training a global encoder network to generate global latent vectors based on training texture images, and training a local encoder network to generate local latent tensors based on the training texture images. The method further includes interpolating between the global latent vectors associated with each set of training images, and interpolating between the local latent tensors associated with each set of training images. The method further includes training a decoder network to generate reconstructions of the training texture images and to generate an interpolated texture based on the interpolated global latent vectors and the interpolated local latent tensors. The training of the encoder and decoder networks is based on a minimization of a loss function of the reconstructions and a minimization of a loss function of the interpolated texture.

    Local neural implicit functions with modulated periodic activations
    17.
    发明授权
    Local neural implicit functions with modulated periodic activations 有权

    公开(公告)号：US12217169B2

    公开(公告)日：2025-02-04

    申请号：US17198670

    申请日：2021-03-11

    Applicant: ADOBE INC.

    Inventor： Ishit bhadresh Mehta ,  Michaël Gharbi ,  Connelly Barnes ,  Elya Shechtman

    IPC: G06N3/04 ,  G06N3/0455 ,  G06N3/048 ,  G06N3/08 ,  G06T3/4007 ,  G06T5/77

    Abstract: Systems and methods for signal processing are described. Embodiments receive a digital signal comprising original signal values corresponding to a discrete set of original sample locations, generate modulation parameters based on the digital signal using a modulator network, wherein each of a plurality of modulator layers of the modulator network outputs a set of the modulation parameters, and generate a predicted signal value of the digital signal at an additional location using a synthesizer network, wherein each of a plurality of synthesizer layers of the synthesizer network operates based on the set of the modulation parameters from a corresponding modulator layer of the modulator network.

    INPAINTING DIGITAL IMAGES USING A HYBRID WIRE REMOVAL PIPELINE
    19.
    发明公开
    INPAINTING DIGITAL IMAGES USING A HYBRID WIRE REMOVAL PIPELINE 审中-公开

    公开(公告)号：US20240303787A1

    公开(公告)日：2024-09-12

    申请号：US18179855

    申请日：2023-03-07

    Applicant: Adobe Inc.

    Inventor： Yuqian Zhou ,  Connelly Barnes ,  Zijun Wei ,  Zhe Lin ,  Elya Shechtman ,  Sohrab Amirghodsi ,  Xiaoyang Liu

    IPC: G06T5/00 ,  G06T7/11 ,  G06V20/10

    CPC classification number: G06T5/77 ,  G06T7/11 ,  G06V20/176 ,  G06T2207/20021 ,  G06T2207/30184

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for inpainting a digital image using a hybrid wire removal pipeline. For example, the disclosed systems use a hybrid wire removal pipeline that integrates multiple machine learning models, such as a wire segmentation model, a hole separation model, a mask dilation model, a patch-based inpainting model, and a deep inpainting model. Using the hybrid wire removal pipeline, in some embodiments, the disclosed systems generate a wire segmentation from a digital image depicting one or more wires. The disclosed systems also utilize the hybrid wire removal pipeline to extract or identify portions of the wire segmentation that indicate specific wires or portions of wires. In certain embodiments, the disclosed systems further inpaint pixels of the digital image corresponding to the wires indicated by the wire segmentation mask using the patch-based inpainting model and/or the deep inpainting model.

    GENERATING MODIFIED DIGITAL IMAGES VIA IMAGE INPAINTING USING MULTI-GUIDED PATCH MATCH AND INTELLIGENT CURATION
    20.
    发明公开
    GENERATING MODIFIED DIGITAL IMAGES VIA IMAGE INPAINTING USING MULTI-GUIDED PATCH MATCH AND INTELLIGENT CURATION 审中-公开

    公开(公告)号：US20230385992A1

    公开(公告)日：2023-11-30

    申请号：US17664991

    申请日：2022-05-25

    Applicant: Adobe Inc.

    Inventor： Connelly Barnes ,  Elya Shechtman ,  Sohrab Amirghodsi ,  Zhe Lin

    IPC: G06T5/00 ,  G06T5/50

    CPC classification number: G06T5/005 ,  G06T5/50 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/10024

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that implement an inpainting framework having computer-implemented machine learning models to generate high-resolution inpainting results. For instance, in one or more embodiments, the disclosed systems generate an inpainted digital image utilizing a deep inpainting neural network from a digital image having a replacement region. The disclosed systems further generate, utilizing a visual guide algorithm, at least one deep visual guide from the inpainted digital image. Using a patch match model and the at least one deep visual guide, the disclosed systems generate a plurality of modified digital images from the digital image by replacing the region of pixels of the digital image with replacement pixels. Additionally, the disclosed systems select, utilizing an inpainting curation model, a modified digital image from the plurality of modified digital images to provide to a client device.

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