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    HIGH-RESOLUTION IMAGE GENERATION
    41.
    发明公开
    HIGH-RESOLUTION IMAGE GENERATION 审中-公开

    公开(公告)号：US20240320789A1

    公开(公告)日：2024-09-26

    申请号：US18585957

    申请日：2024-02-23

    Applicant: ADOBE INC.

    Inventor： Tobias Hinz ,  Taesung Park ,  Jingwan Lu ,  Elya Shechtman ,  Richard Zhang ,  Oliver Wang

    IPC: G06T3/4053 ,  G06T3/4046 ,  G06T11/00

    CPC classification number: G06T3/4053 ,  G06T3/4046 ,  G06T11/00

    Abstract: A method, non-transitory computer readable medium, apparatus, and system for image generation include obtaining an input image having a first resolution, where the input image includes random noise, and generating a low-resolution image based on the input image, where the low-resolution image has the first resolution. The method, non-transitory computer readable medium, apparatus, and system further include generating a high-resolution image based on the low-resolution image, where the high-resolution image has a second resolution that is greater than the first resolution.

    Few-shot image generation via self-adaptation
    43.
    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
    45.
    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
    46.
    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.

    GENERATING MODIFIED DIGITAL IMAGES INCORPORATING SCENE LAYOUT UTILIZING A SWAPPING AUTOENCODER
    47.
    发明公开
    GENERATING MODIFIED DIGITAL IMAGES INCORPORATING SCENE LAYOUT UTILIZING A SWAPPING AUTOENCODER 审中-公开

    公开(公告)号：US20230245363A1

    公开(公告)日：2023-08-03

    申请号：US18298138

    申请日：2023-04-10

    Applicant: Adobe Inc.

    Inventor： Taesung Park ,  Alexei A. Efros ,  Elya Shechtman ,  Richard Zhang ,  Junyan Zhu

    IPC: G06T11/60 ,  G06N3/088 ,  G06T7/10 ,  G06N3/045

    CPC classification number: G06T11/60 ,  G06N3/088 ,  G06T7/10 ,  G06N3/045

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating modified digital images utilizing a novel swapping autoencoder that incorporates scene layout. In particular, the disclosed systems can receive a scene layout map that indicates or defines locations for displaying specific digital content within a digital image. In addition, the disclosed systems can utilize the scene layout map to guide combining portions of digital image latent code to generate a modified digital image with a particular textural appearance and a particular geometric structure defined by the scene layout map. Additionally, the disclosed systems can utilize a scene layout map that defines a portion of a digital image to modify by, for instance, adding new digital content to the digital image, and can generate a modified digital image depicting the new digital content.

    Generating shift-invariant neural network feature maps and outputs
    48.
    发明授权
    Generating shift-invariant neural network feature maps and outputs 有权

    公开(公告)号：US11562166B2

    公开(公告)日：2023-01-24

    申请号：US17327088

    申请日：2021-05-21

    Applicant: Adobe Inc.

    Inventor： Richard Zhang

    IPC: G06K9/00 ,  G06N3/08 ,  G06N20/10 ,  G06T5/00

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating shift-resilient neural network outputs based on utilizing a dense pooling layer, a low-pass filter layer, and a downsampling layer of a neural network. For example, the disclosed systems can generate a pooled feature map utilizing a dense pooling layer to densely pool feature values extracted from an input. The disclosed systems can further apply a low-pass filter to the pooled feature map to generate a shift-adaptive feature map. In addition, the disclosed systems can downsample the shift-adaptive feature map utilizing a downsampling layer. Based on the downsampled, shift-adaptive feature map, the disclosed systems can generate shift-resilient neural network outputs such as digital image classifications.

    Modifying neural networks for synthetic conditional digital content generation utilizing contrastive perceptual loss
    50.
    发明授权
    Modifying neural networks for synthetic conditional digital content generation utilizing contrastive perceptual loss 有权

    公开(公告)号：US11514632B2

    公开(公告)日：2022-11-29

    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: G09G5/00 ,  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.

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