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    Systems and methods for facial image generation
    11.
    发明授权
    Systems and methods for facial image generation 有权

    公开(公告)号：US11941727B2

    公开(公告)日：2024-03-26

    申请号：US17813987

    申请日：2022-07-21

    Applicant: ADOBE INC.

    Inventor： Saeid Motiian ,  Wei-An Lin ,  Shabnam Ghadar

    IPC: G06T11/00 ,  G06V40/16

    CPC classification number: G06T11/00 ,  G06V40/168 ,  G06T2200/24

    Abstract: Systems and methods for facial image generation are described. One aspect of the systems and methods includes receiving an image depicting a face, wherein the face has an identity non-related attribute and a first identity-related attribute; encoding the image to obtain an identity non-related attribute vector in an identity non-related attribute vector space, wherein the identity non-related attribute vector represents the identity non-related attribute; selecting an identity-related vector from an identity-related vector space, wherein the identity-related vector represents a second identity-related attribute different from the first identity-related attribute; generating a modified latent vector in a latent vector space based on the identity non-related attribute vector and the identity-related vector; and generating a modified image based on the modified latent vector, wherein the modified image depicts a face that has the identity non-related attribute and the second identity-related attribute.

    SUPERVISED LEARNING TECHNIQUES FOR ENCODER TRAINING
    15.
    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.

    NON-LINEAR LATENT FILTER TECHNIQUES FOR IMAGE EDITING
    16.
    发明申请
    NON-LINEAR LATENT FILTER TECHNIQUES FOR IMAGE EDITING 有权

    公开(公告)号：US20220121876A1

    公开(公告)日：2022-04-21

    申请号：US17468498

    申请日：2021-09-07

    Applicant: Adobe Inc.

    Inventor： Ratheesh Kalarot ,  Wei-An Lin ,  Baldo Faieta ,  Shabnam Ghadar

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20

    Abstract: Systems and methods use a non-linear latent filter neural network for editing an image. An image editing system trains a first neural network by minimizing a loss based upon a predicted attribute value for a target attribute in a training image. The image editing system obtains a latent space representation of an input image to be edited and a target attribute value for the target attribute in the input image. The image editing system provides the latent space representation and the target attribute value as input to the trained first neural network for modifying the target attribute in the input image to generate a modified latent space representation of the input image. The image editing system provides the modified latent space representation as input to a second neural network to generate an output image with a modification to the target attribute corresponding to the target attribute value.

    Enhancing detailed segments in latent code-based edited digital images
    17.
    发明授权
    Enhancing detailed segments in latent code-based edited digital images 有权

    公开(公告)号：US12254594B2

    公开(公告)日：2025-03-18

    申请号：US17657691

    申请日：2022-04-01

    Applicant: Adobe Inc.

    Inventor： Hui Qu ,  Jingwan Lu ,  Saeid Motiian ,  Shabnam Ghadar ,  Wei-An Lin ,  Elya Shechtman

    IPC: G06T5/50 ,  G06N3/04 ,  G06N3/045 ,  G06T7/11

    Abstract: Methods, systems, and non-transitory computer readable media are disclosed for intelligently enhancing details in edited images. The disclosed system iteratively updates residual detail latent code for segments in edited images where detail has been lost through the editing process. More particularly, the disclosed system enhances an edited segment in an edited image based on details in a detailed segment of an image. Additionally, the disclosed system may utilize a detail neural network encoder to project the detailed segment and a corresponding segment of the edited image into a residual detail latent code. In some embodiments, the disclosed system generates a refined edited image based on the residual detail latent code and a latent vector of the edited image.

    IMAGE RELIGHTING
    18.
    发明申请
    IMAGE RELIGHTING 有权

    公开(公告)号：US20250069299A1

    公开(公告)日：2025-02-27

    申请号：US18452827

    申请日：2023-08-21

    Applicant: ADOBE INC.

    Inventor： Kevin Duarte ,  Wei-An Lin ,  Ratheesh Kalarot ,  Shabnam Ghadar ,  Jingwan Lu ,  Elya Shechtman

    IPC: G06T11/60

    Abstract: One or more aspects of a method, apparatus, and non-transitory computer readable medium include obtaining an input latent vector for an image generation network and a target lighting representation. A modified latent vector is generated based on the input latent vector and the target lighting representation, and an image generation network generates an image based on the modified latent vector using.

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