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    User-guided image generation
    1.
    发明授权
    User-guided image generation 有权

    公开(公告)号：US12230014B2

    公开(公告)日：2025-02-18

    申请号：US17680906

    申请日：2022-02-25

    Applicant: ADOBE INC.

    Inventor： Yijun Li ,  Utkarsh Ojha ,  Richard Zhang ,  Jingwan Lu ,  Elya Shechtman ,  Alexei A. Efros

    IPC: G06V10/774 ,  G06F3/04842

    Abstract: An image generation system enables user input during the process of training a generative model to influence the model's ability to generate new images with desired visual features. A source generative model for a source domain is fine-tuned using training images in a target domain to provide an adapted generative model for the target domain. Interpretable factors are determined for the source generative model and the adapted generative model. A user interface is provided that enables a user to select one or more interpretable factors. The user-selected interpretable factor(s) are used to generate a user-adapted generative model, for instance, by using a loss function based on the user-selected interpretable factor(s). The user-adapted generative model can be used to create new images in the target domain.

    Few-shot image generation via self-adaptation
    2.
    发明授权
    Few-shot image generation via self-adaptation 有权

    公开(公告)号：US11880957B2

    公开(公告)日：2024-01-23

    申请号：US17013332

    申请日：2020-09-04

    Applicant: Adobe Inc.

    Inventor： Yijun Li ,  Richard Zhang ,  Jingwan Lu ,  Elya Shechtman

    IPC: G06T3/00 ,  G06N20/00 ,  G06T11/00

    CPC classification number: G06T3/0056 ,  G06N20/00 ,  G06T11/00 ,  G06T2207/20081

    Abstract: One example method involves operations for receiving a request to transform an input image into a target image. Operations further include providing the input image to a machine learning model trained to adapt images. Training the machine learning model includes accessing training data having a source domain of images and a target domain of images with a target style. Training further includes using a pre-trained generative model to generate an adapted source domain of adapted images having the target style. The adapted source domain is generated by determining a rate of change for parameters of the target style, generating weighted parameters by applying a weight to each of the parameters based on their respective rate of change, and applying the weighted parameters to the source domain. Additionally, operations include using the machine learning model to generate the target image by modifying parameters of the input image using the target style.

    Few-shot digital image generation using gan-to-gan translation
    4.
    发明授权
    Few-shot digital image generation using gan-to-gan translation 有权

    公开(公告)号：US11763495B2

    公开(公告)日：2023-09-19

    申请号：US17163284

    申请日：2021-01-29

    Applicant: Adobe Inc.

    Inventor： Utkarsh Ojha ,  Yijun Li ,  Richard Zhang ,  Jingwan Lu ,  Elya Shechtman ,  Alexei A. Efros

    IPC: G06T11/00 ,  G06N3/02 ,  G06F18/22 ,  G06F18/214

    CPC classification number: G06T11/00 ,  G06F18/214 ,  G06F18/22 ,  G06N3/02

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and efficiently modifying a generative adversarial neural network using few-shot adaptation to generate digital images corresponding to a target domain while maintaining diversity of a source domain and realism of the target domain. In particular, the disclosed systems utilize a generative adversarial neural network with parameters learned from a large source domain. The disclosed systems preserve relative similarities and differences between digital images in the source domain using a cross-domain distance consistency loss. In addition, the disclosed systems utilize an anchor-based strategy to encourage different levels or measures of realism over digital images generated from latent vectors in different regions of a latent space.

    Image Inversion Using Multiple Latent Spaces
    5.
    发明公开
    Image Inversion Using Multiple Latent Spaces 审中-公开

    公开(公告)号：US20230289970A1

    公开(公告)日：2023-09-14

    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 ,  G06T11/00 ,  G06T3/40

    CPC classification number: G06T7/11 ,  G06T11/001 ,  G06T3/4046 ,  G06T2207/20084 ,  G06T2207/20081

    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.

    UNIVERSAL STYLE TRANSFER USING MULTI-SCALE FEATURE TRANSFORM AND USER CONTROLS
    6.
    发明申请
    UNIVERSAL STYLE TRANSFER USING MULTI-SCALE FEATURE TRANSFORM AND USER CONTROLS 有权

    公开(公告)号：US20230082050A1

    公开(公告)日：2023-03-16

    申请号：US17447893

    申请日：2021-09-16

    Applicant: Adobe Inc.

    Inventor： Yijun Li ,  Ionut Mironicã

    IPC: G06T5/50 ,  G06T11/00 ,  G06T5/00 ,  G06T7/50 ,  G06N3/04

    Abstract: Techniques for generating style-transferred images are provided. In some embodiments, a content image, a style image, and a user input indicating one or more modifications that operate on style-transferred images are received. In some embodiments, an initial style-transferred image is generated using a machine learning model. In some examples, the initial style-transferred image comprises features associated with the style image applied to content included in the content image. In some embodiments, a modified style-transferred image is generated by modifying the initial style-transferred image based at least in part on the user input indicating the one or more modifications.

    Generating stylized-stroke images from source images utilizing style-transfer-neural networks with non-photorealistic-rendering
    7.
    发明授权
    Generating stylized-stroke images from source images utilizing style-transfer-neural networks with non-photorealistic-rendering 有权

    公开(公告)号：US10748324B2

    公开(公告)日：2020-08-18

    申请号：US16184289

    申请日：2018-11-08

    Applicant: Adobe Inc.

    Inventor： Elya Shechtman ,  Yijun Li ,  Chen Fang ,  Aaron Hertzmann

    IPC: G06T15/02 ,  G06T15/80 ,  G06N3/08 ,  G06N3/04

    Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that integrate (or embed) a non-photorealistic rendering (“NPR”) generator with a style-transfer-neural network to generate stylized images that both correspond to a source image and resemble a stroke style. By integrating an NPR generator with a style-transfer-neural network, the disclosed methods, non-transitory computer readable media, and systems can accurately capture a stroke style resembling one or both of stylized edges or stylized shadings. When training such a style-transfer-neural network, the integrated NPR generator can enable the disclosed methods, non-transitory computer readable media, and systems to use real-stroke drawings (instead of conventional paired-ground-truth drawings) for training the network to accurately portray a stroke style. In some implementations, the disclosed methods, non-transitory computer readable media, and systems can either train or apply a style-transfer-neural network that captures a variety of stroke styles, such as different edge-stroke styles or shading-stroke styles.

    RESTORING DEGRADED DIGITAL IMAGES THROUGH A DEEP LEARNING FRAMEWORK
    8.
    发明申请
    RESTORING DEGRADED DIGITAL IMAGES THROUGH A DEEP LEARNING FRAMEWORK 有权

    公开(公告)号：US20250069204A1

    公开(公告)日：2025-02-27

    申请号：US18944363

    申请日：2024-11-12

    Applicant: Adobe Inc.

    Inventor： Ionut Mironica ,  Yijun Li

    IPC: G06T5/77 ,  G06N3/045 ,  G06N3/08 ,  G06T3/18 ,  G06T5/50 ,  G06T5/70 ,  G06T5/73 ,  G06T7/11

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly restoring degraded digital images utilizing a deep learning framework for repairing local defects, correcting global imperfections, and/or enhancing depicted faces. In particular, the disclosed systems can utilize a defect detection neural network to generate a segmentation map indicating locations of local defects within a digital image. In addition, the disclosed systems can utilize an inpainting algorithm to determine pixels for inpainting the local defects to reduce their appearance. In some embodiments, the disclosed systems utilize a global correction neural network to determine and repair global imperfections. Further, the disclosed systems can enhance one or more faces depicted within a digital image utilizing a face enhancement neural network as well.

    GAN IMAGE GENERATION FROM FEATURE REGULARIZATION
    9.
    发明申请
    GAN IMAGE GENERATION FROM FEATURE REGULARIZATION 有权

    公开(公告)号：US20250037431A1

    公开(公告)日：2025-01-30

    申请号：US18357621

    申请日：2023-07-24

    Applicant: ADOBE INC.

    Inventor： Min Jin Chong ,  Krishna Kumar Singh ,  Yijun Li ,  Jingwan Lu

    IPC: G06V10/774 ,  G06N3/045 ,  G06N3/0475

    Abstract: Systems and methods for training a Generative Adversarial Network (GAN) using feature regularization are described herein. Embodiments are configured to generate a candidate image using a generator network of a GAN, classify the candidate image as real or generated using a discriminator network of the GAN, and train the GAN to generate realistic images based on the classifying of the candidate image. The training process includes regularizing a gradient with respect to features extracted using a discriminator network of the GAN.

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