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    MODIFYING NEURAL NETWORKS FOR SYNTHETIC CONDITIONAL DIGITAL CONTENT GENERATION UTILIZING CONTRASTIVE PERCEPTUAL LOSS
    21.
    发明申请
    MODIFYING NEURAL NETWORKS FOR SYNTHETIC CONDITIONAL DIGITAL CONTENT GENERATION UTILIZING CONTRASTIVE PERCEPTUAL LOSS 有权

    公开(公告)号：US20220148242A1

    公开(公告)日：2022-05-12

    申请号：US17091440

    申请日：2020-11-06

    Applicant: Adobe Inc.

    Inventor： Bryan Russell ,  Taesung Park ,  Richard Zhang ,  Junyan Zhu ,  Alexander Andonian

    IPC: G06T11/60 ,  G06N3/04

    Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize a contrastive perceptual loss to modify neural networks for generating synthetic digital content items. For example, the disclosed systems generate a synthetic digital content item based on a guide input to a generative neural network. The disclosed systems utilize an encoder neural network to generate encoded representations of the synthetic digital content item and a corresponding ground-truth digital content item. Additionally, the disclosed systems sample patches from the encoded representations of the encoded digital content items and then determine a contrastive loss based on the perceptual distances between the patches in the encoded representations. Furthermore, the disclosed systems jointly update the parameters of the generative neural network and the encoder neural network utilizing the contrastive loss.

    DIRECT REGRESSION ENCODER ARCHITECTURE AND TRAINING
    24.
    发明申请
    DIRECT REGRESSION ENCODER ARCHITECTURE AND TRAINING 有权

    公开(公告)号：US20220121931A1

    公开(公告)日：2022-04-21

    申请号：US17384371

    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 ,  G06K9/62

    Abstract: Systems and methods train and apply a specialized encoder neural network for fast and accurate projection into the latent space of a Generative Adversarial Network (GAN). The specialized encoder neural network includes an input layer, a feature extraction layer, and a bottleneck layer positioned after the feature extraction layer. The projection process includes providing an input image to the encoder and producing, by the encoder, a latent space representation of the input image. Producing the latent space representation includes extracting a feature vector from the feature extraction layer, providing the feature vector to the bottleneck layer as input, and producing the latent space representation as output. The latent space representation produced by the encoder is provided as input to the GAN, which generates an output image based upon the latent space representation. The encoder is trained using specialized loss functions including a segmentation loss and a mean latent loss.

    AUTOMATIC MAKEUP TRANSFER USING SEMI-SUPERVISED LEARNING
    25.
    发明申请
    AUTOMATIC MAKEUP TRANSFER USING SEMI-SUPERVISED LEARNING 有权

    公开(公告)号：US20210295045A1

    公开(公告)日：2021-09-23

    申请号：US16822878

    申请日：2020-03-18

    Applicant: Adobe Inc.

    Inventor： Yijun Li ,  Zhifei Zhang ,  Richard Zhang ,  Jingwan Lu

    IPC: G06K9/00 ,  G06K9/62 ,  G06K9/46 ,  G06N3/08 ,  G06N3/04

    Abstract: The present disclosure relates to systems, computer-implemented methods, and non-transitory computer readable medium for automatically transferring makeup from a reference face image to a target face image using a neural network trained using semi-supervised learning. For example, the disclosed systems can receive, at a neural network, a target face image and a reference face image, where the target face image is selected by a user via a graphical user interface (GUI) and the reference face image has makeup. The systems transfer, by the neural network, the makeup from the reference face image to the target face image, where the neural network is trained to transfer the makeup from the reference face image to the target face image using semi-supervised learning. The systems output for display the makeup on the target face image.

    Image inversion using multiple latent spaces
    26.
    发明授权
    Image inversion using multiple latent spaces 有权

    公开(公告)号：US12159413B2

    公开(公告)日：2024-12-03

    申请号：US17693618

    申请日：2022-03-14

    Applicant: Adobe Inc.

    Inventor： Gaurav Parmar ,  Krishna Kumar Singh ,  Yijun Li ,  Richard Zhang ,  Jingwan Lu

    IPC: G06T7/11 ,  G06T3/4046 ,  G06T11/00

    Abstract: In implementations of systems for image inversion using multiple latent spaces, a computing device implements an inversion system to generate a segment map that segments an input digital image into a first image region and a second image region and assigns the first image region to a first latent space and the second image region to a second latent space that corresponds to a layer of a convolutional neural network. An inverted latent representation of the input digital image is computed using a binary mask for the second image region. The inversion system modifies the inverted latent representation of the input digital image using an edit direction vector that corresponds to a visual feature. An output digital image is generated that depicts a reconstruction of the input digital image having the visual feature based on the modified inverted latent representation of the input digital image.

    SUPER-RESOLUTION ON TEXT-TO-IMAGE SYNTHESIS WITH GANS
    29.
    发明公开
    SUPER-RESOLUTION ON TEXT-TO-IMAGE SYNTHESIS WITH GANS 审中-公开

    公开(公告)号：US20240281924A1

    公开(公告)日：2024-08-22

    申请号：US18171046

    申请日：2023-02-17

    Applicant: ADOBE INC.

    Inventor： Taesung Park ,  Minguk Kang ,  Richard Zhang ,  Junyan Zhu ,  Elya Shechtman ,  Sylvain Paris

    IPC: G06T3/40

    CPC classification number: G06T3/4046 ,  G06T3/4053

    Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure obtain a low-resolution image and a text description of the low-resolution image. A mapping network generates a style vector representing the text description of the low-resolution image. An adaptive convolution component generates an adaptive convolution filter based on the style vector. An image generation network generates a high-resolution image corresponding to the low-resolution image based on the adaptive convolution filter.

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