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    OBJECT CLASS INPAINTING IN DIGITAL IMAGES UTILIZING CLASS-SPECIFIC INPAINTING NEURAL NETWORKS
    12.
    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.

    Generative image congealing
    13.
    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

    Applicant: Adobe Inc.

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

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

    CPC classification number: G06F18/217 ,  G06F18/214 ,  G06N3/045 ,  G06N3/08 ,  G06T3/0068

    Abstract: 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.

    GENERATING SHADOWS FOR DIGITAL OBJECTS WITHIN DIGITAL IMAGES UTILIZING A HEIGHT MAP
    15.
    发明申请
    GENERATING SHADOWS FOR DIGITAL OBJECTS WITHIN DIGITAL IMAGES UTILIZING A HEIGHT MAP 有权

    公开(公告)号：US20230123658A1

    公开(公告)日：2023-04-20

    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 ,  G06T11/00 ,  G06T7/70 ,  G06T7/194 ,  G06N3/04

    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.

    Iterative image inpainting with confidence feedback
    17.
    发明授权
    Iterative image inpainting with confidence feedback 有权

    公开(公告)号：US11605156B2

    公开(公告)日：2023-03-14

    申请号：US17812639

    申请日：2022-07-14

    Applicant: ADOBE INC.

    Inventor： Zhe Lin ,  Yu Zeng ,  Jimei Yang ,  Jianming Zhang ,  Elya Shechtman

    IPC: G06T5/00 ,  G06N3/04 ,  G06N3/08

    Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of images using iterative image inpainting. In particular, iterative inpainting utilize a confidence analysis of predicted pixels determined during the iterations of inpainting. For instance, a confidence analysis can provide information that can be used as feedback to progressively fill undefined pixels that comprise the holes, regions, and/or portions of an image where information for those respective pixels is not known. To allow for accurate image inpainting, one or more neural networks can be used. For instance, a coarse result neural network (e.g., a GAN comprised of a generator and a discriminator) and a fine result neural network (e.g., a GAN comprised of a generator and two discriminators). The image inpainting system can use such networks to predict an inpainting image result that fills the hole, region, and/or portion of the image using predicted pixels and generates a corresponding confidence map of the predicted pixels.

    Image modification using detected symmetry
    18.
    发明授权
    Image modification using detected symmetry 有权

    公开(公告)号：US11551388B2

    公开(公告)日：2023-01-10

    申请号：US16794908

    申请日：2020-02-19

    Applicant: Adobe Inc.

    Inventor： Kalyan Krishna Sunkavalli ,  Nathan Aaron Carr ,  Michal Lukác ,  Elya Shechtman

    IPC: G06T7/68 ,  G06T11/60 ,  G06K9/62 ,  G06T5/00 ,  G06T7/49 ,  G06V10/42 ,  G06V10/75

    Abstract: Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map. The image modification module produces a manipulated image by manipulating the original image under global symmetry constraints imposed by the global symmetry association map.

    STYLE-AWARE AUDIO-DRIVEN TALKING HEAD ANIMATION FROM A SINGLE IMAGE
    19.
    发明申请
    STYLE-AWARE AUDIO-DRIVEN TALKING HEAD ANIMATION FROM A SINGLE IMAGE 有权

    公开(公告)号：US20220392131A1

    公开(公告)日：2022-12-08

    申请号：US17887685

    申请日：2022-08-15

    Applicant: Adobe Inc.

    Inventor： Dingzeyu Li ,  Yang Zhou ,  Jose Ignacio Echevarria Vallespi ,  Elya Shechtman

    IPC: G06T13/20 ,  G06T17/20 ,  G06T13/40

    Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for generating an animation of a talking head from an input audio signal of speech and a representation (such as a static image) of a head to animate. Generally, a neural network can learn to predict a set of 3D facial landmarks that can be used to drive the animation. In some embodiments, the neural network can learn to detect different speaking styles in the input speech and account for the different speaking styles when predicting the 3D facial landmarks. Generally, template 3D facial landmarks can be identified or extracted from the input image or other representation of the head, and the template 3D facial landmarks can be used with successive windows of audio from the input speech to predict 3D facial landmarks and generate a corresponding animation with plausible 3D effects.

    Labeling techniques for a modified panoptic labeling neural network
    20.
    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.

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