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    Semantic Image Fill at High Resolutions
    3.
    发明公开
    Semantic Image Fill at High Resolutions 审中-公开

    公开(公告)号：US20230360376A1

    公开(公告)日：2023-11-09

    申请号：US17744995

    申请日：2022-05-16

    Applicant: Adobe Inc.

    Inventor： Tobias Hinz ,  Taesung Park ,  Richard Zhang ,  Matthew David Fisher ,  Difan Liu ,  Evangelos Kalogerakis

    IPC: G06V10/774 ,  G06V10/22 ,  G06T3/40

    CPC classification number: G06V10/7753 ,  G06V10/235 ,  G06T3/4046

    Abstract: Semantic fill techniques are described that support generating fill and editing images from semantic inputs. A user input, for example, is received by a semantic fill system that indicates a selection of a first region of a digital image and a corresponding semantic label. The user input is utilized by the semantic fill system to generate a guidance attention map of the digital image. The semantic fill system leverages the guidance attention map to generate a sparse attention map of a second region of the digital image. A semantic fill of pixels is generated for the first region based on the semantic label and the sparse attention map. The edited digital image is displayed in a user interface.

    Generative image congealing
    4.
    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.

    Projecting images to a generative model based on gradient-free latent vector determination
    6.
    发明授权
    Projecting images to a generative model based on gradient-free latent vector determination 有权

    公开(公告)号：US11468294B2

    公开(公告)日：2022-10-11

    申请号：US16798271

    申请日：2020-02-21

    Applicant: Adobe Inc.

    Inventor： Richard Zhang ,  Sylvain Philippe Paris ,  Junyan Zhu ,  Aaron Phillip Hertzmann ,  Jacob Minyoung Huh

    IPC: G06N3/04 ,  G06T11/60 ,  G06F17/18

    Abstract: A target image is projected into a latent space of generative model by determining a latent vector by applying a gradient-free technique and a class vector by applying a gradient-based technique. An image is generated from the latent and class vectors, and a loss function is used to determine a loss between the target image and the generated image. This determining of the latent vector and the class vector, generating an image, and using the loss function is repeated until a loss condition is satisfied. In response to the loss condition being satisfied, the latent and class vectors that resulted in the loss condition being satisfied are identified as the final latent and class vectors, respectively. The final latent and class vectors are provided to the generative model and multiple weights of the generative model are adjusted to fine-tune the generative model.

    SUPERVISED LEARNING TECHNIQUES FOR ENCODER TRAINING
    9.
    发明申请
    SUPERVISED LEARNING TECHNIQUES FOR ENCODER TRAINING 有权

    公开(公告)号：US20220121932A1

    公开(公告)日：2022-04-21

    申请号：US17384378

    申请日：2021-07-23

    Applicant: Adobe Inc.

    Inventor： Ratheesh Kalarot ,  Wei-An Lin ,  Cameron Smith ,  Zhixin Shu ,  Baldo Faieta ,  Shabnam Ghadar ,  Jingwan Lu ,  Aliakbar Darabi ,  Jun-Yan Zhu ,  Niloy Mitra ,  Richard Zhang ,  Elya Shechtman

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Systems and methods train an encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The encoder is trained by providing an input training image to the encoder and producing, by the encoder, a latent space representation of the input training image. The latent space representation is provided as input to the GAN to generate a generated training image. A latent code is sampled from a latent space associated with the GAN and the sampled latent code is provided as input to the GAN. The GAN generates a synthetic training image based on the sampled latent code. The sampled latent code is provided as input to the encoder to produce a synthetic training code. The encoder is updated by minimizing a loss between the generated training image and the input training image, and the synthetic training code and the sampled latent code.

    Adding color to digital images
    10.
    发明授权
    Adding color to digital images 有权

    公开(公告)号：US11232607B2

    公开(公告)日：2022-01-25

    申请号：US16751959

    申请日：2020-01-24

    Applicant: Adobe Inc.

    Inventor： Nishant Kumar ,  Vikas Sharma ,  Shantanu Agarwal ,  Sameer Bhatt ,  Rupali Arora ,  Richard Zhang ,  Anuradha Yadav ,  Jingwan Lu ,  Matthew David Fisher

    IPC: G06T11/00 ,  G06T7/90 ,  G06K9/46 ,  H04N1/60

    Abstract: In implementations of adding color to digital images, an image colorization system can display a digital image to be color adjusted in an image editing interface and convert pixel content of the digital image to a LAB color space. The image colorization system can determine a lightness value (L) in the LAB color space of the pixel content of the digital image at a specified point on the digital image, and determine colors representable in an RGB color space based on combinations of A,B value pairs with the lightness value (L) in the LAB color space. The image colorization system can then determine a range of the colors for display in a color gamut in the image editing interface, the range of the colors corresponding to the A,B value pairs with the lightness value (L) of the pixel content at the specified point on the digital image.

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