公开(公告)号：US20250124544A1

公开(公告)日：2025-04-17

申请号：US18487764

申请日：2023-10-16

Applicant: ADOBE INC.

Inventor： Taesung Park ,  Qing Liu ,  Zhe Lin ,  Sohrab Amirghodsi ,  Elya Shechtman

IPC: G06T3/40 ,  G06T5/50 ,  G06T11/00

Abstract: Systems and methods for upsampling low-resolution content within a high-resolution image include obtaining a composite image and a mask. The composite image includes a high-resolution region and a low-resolution region. An upsampling network identifies the low-resolution region of the composite image based on the mask and generates an upsampled composite image based on the composite image and the mask. The upsampled composite image comprises higher frequency details in the low-resolution region than the composite image.

公开(公告)号：US20250045994A1

公开(公告)日：2025-02-06

申请号：US18924508

申请日：2024-10-23

Applicant: Adobe Inc.

Inventor： Nadav Epstein ,  Alexei A Efros ,  Taesung Park ,  Richard Zhang ,  Elya Shechtman

IPC: G06T11/60 ,  G06T7/90

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital images depicting photorealistic scenes utilizing a digital image collaging neural network. For example, the disclosed systems utilize a digital image collaging neural network having a particular architecture for disentangling generation of scene layouts and pixel colors for different regions of a digital image. In some cases, the disclosed systems break down the process of generating a collage digital into generating images representing different regions such as a background and a foreground to be collaged into a final result. For example, utilizing the digital image collaging neural network, the disclosed systems determine scene layouts and pixel colors for both foreground digital images and background digital images to ultimately collage the foreground and background together into a collage digital image depicting a real-world scene.

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.

公开(公告)号：US20250069203A1

公开(公告)日：2025-02-27

申请号：US18454850

申请日：2023-08-24

Applicant: ADOBE INC.

Inventor： Yuqian Zhou ,  Krishna Kumar Singh ,  Benjamin Delarre ,  Zhe Lin ,  Jingwan Lu ,  Taesung Park ,  Sohrab Amirghodsi ,  Elya Shechtman

IPC: G06T5/00 ,  G06F40/40 ,  G06T11/00

Abstract: A method, non-transitory computer readable medium, apparatus, and system for image generation are described. An embodiment of the present disclosure includes obtaining an input image, an inpainting mask, and a plurality of content preservation values corresponding to different regions of the inpainting mask, and identifying a plurality of mask bands of the inpainting mask based on the plurality of content preservation values. An image generation model generates an output image based on the input image and the inpainting mask. The output image is generated in a plurality of phases. Each of the plurality of phases uses a corresponding mask band of the plurality of mask bands as an input.

公开(公告)号：US20240354895A1

公开(公告)日：2024-10-24

申请号：US18303271

申请日：2023-04-19

Applicant: ADOBE INC.

Inventor： Hareesh Ravi ,  Midhun Harikumar ,  Taesung Park ,  Ajinkya Gorakhnath Kale

IPC: G06T5/50 ,  G06T5/00 ,  G06T11/60 ,  G06V10/764

CPC classification number: G06T5/50 ,  G06T5/00 ,  G06T11/60 ,  G06V10/764 ,  G06T2200/24 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20092 ,  G06T2207/20212

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure include an image generation network configured to encode a plurality of abstract images using a style encoder to obtain a plurality of abstract style encodings, wherein the style encoder is trained to represent image style separately from image content. A clustering component clusters the plurality of abstract style encodings to obtain an abstract style cluster comprising a subset of the plurality of abstract style encodings. A preset component generates an abstract style transfer preset representing the abstract style cluster.

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.

公开(公告)号：US20240169621A1

公开(公告)日：2024-05-23

申请号：US18056579

申请日：2022-11-17

Applicant: ADOBE INC.

Inventor： Yotam Nitzan ,  Taesung Park ,  Michaël Gharbi ,  Richard Zhang ,  Junyan Zhu ,  Elya Shechtman

IPC: G06T11/60 ,  G06V10/774

CPC classification number: G06T11/60 ,  G06V10/774 ,  G06T2200/24 ,  G06V10/82

Abstract: Systems and methods for image generation include obtaining an input image and an attribute value representing an attribute of the input image to be modified; computing a modified latent vector for the input image by applying the attribute value to a basis vector corresponding to the attribute in a latent space of an image generation network; and generating a modified image based on the modified latent vector using the image generation network, wherein the modified image includes the attribute based on the attribute value.

公开(公告)号：US20240161462A1

公开(公告)日：2024-05-16

申请号：US18053556

申请日：2022-11-08

Applicant: ADOBE INC.

Inventor： Yosef Gandelsman ,  Taesung Park ,  Richard Zhang ,  Elya Shechtman

IPC: G06V10/774 ,  G06T5/00 ,  G06T11/00 ,  G06V10/776 ,  G06V10/82 ,  G06V10/94

CPC classification number: G06V10/774 ,  G06T5/002 ,  G06T11/00 ,  G06V10/776 ,  G06V10/82 ,  G06V10/945 ,  G06T2200/24 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for image editing are described. Embodiments of the present disclosure include obtaining an image and a prompt for editing the image. A diffusion model is tuned based on the image to generate different versions of the image. The prompt is then encoded to obtain a guidance vector, and the diffusion model generates a modified image based on the image and the encoded text prompt.

公开(公告)号：US20230260175A1

公开(公告)日：2023-08-17

申请号：US17650957

申请日：2022-02-14

Applicant: Adobe Inc.

Inventor： Nadav Epstein ,  Alexei A. Efros ,  Taesung Park ,  Richard Zhang ,  Elya Shechtman

IPC: G06T11/60 ,  G06T7/90

CPC classification number: G06T11/60 ,  G06T7/90 ,  G06T2207/20084 ,  G06T2207/20212

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital images depicting photorealistic scenes utilizing a digital image collaging neural network. For example, the disclosed systems utilize a digital image collaging neural network having a particular architecture for disentangling generation of scene layouts and pixel colors for different regions of a digital image. In some cases, the disclosed systems break down the process of generating a collage digital into generating images representing different regions such as a background and a foreground to be collaged into a final result. For example, utilizing the digital image collaging neural network, the disclosed systems determine scene layouts and pixel colors for both foreground digital images and background digital images to ultimately collage the foreground and background together into a collage digital image depicting a real-world scene.

公开(公告)号：US11625875B2

公开(公告)日：2023-04-11

申请号：US17091416

申请日：2020-11-06

Applicant: Adobe Inc.

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

IPC: G06T11/60 ,  G06T7/10 ,  G06N3/088 ,  G06N3/04

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.