公开(公告)号：US20250022252A1

公开(公告)日：2025-01-16

申请号：US18899571

申请日：2024-09-27

Applicant: Adobe Inc.

Inventor： Khoi Pham ,  Kushal Kafle ,  Zhe Lin ,  Zhihong Ding ,  Scott Cohen ,  Quan Tran

IPC: G06V10/75 ,  G06F18/214 ,  G06F18/25 ,  G06N3/08

Abstract: This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that extract multiple attributes from an object portrayed in a digital image utilizing a multi-attribute contrastive classification neural network. For example, the disclosed systems utilize a multi-attribute contrastive classification neural network that includes an embedding neural network, a localizer neural network, a multi-attention neural network, and a classifier neural network. In some cases, the disclosed systems train the multi-attribute contrastive classification neural network utilizing a multi-attribute, supervised-contrastive loss. In some embodiments, the disclosed systems generate negative attribute training labels for labeled digital images utilizing positive attribute labels that correspond to the labeled digital images.

公开(公告)号：US20250022099A1

公开(公告)日：2025-01-16

申请号：US18351838

申请日：2023-07-13

Applicant: ADOBE INC.

Inventor： Yizhi Song ,  Zhifei Zhang ,  Zhe Lin ,  Scott Cohen ,  Brian Lynn Price ,  Jianming Zhang ,  Soo Ye Kim

IPC: G06T5/50 ,  G06T1/00 ,  G06T5/00 ,  G06T9/00

Abstract: Systems and methods for image compositing are provided. An aspect of the systems and methods includes obtaining a first image and a second image, wherein the first image includes a target location and the second image includes a target element; encoding the second image using an image encoder to obtain an image embedding; generating a descriptive embedding based on the image embedding using an adapter network; and generating a composite image based on the descriptive embedding and the first image using an image generation model, wherein the composite image depicts the target element from the second image at the target location of the first image.

公开(公告)号：US12165295B2

公开(公告)日：2024-12-10

申请号：US17661985

申请日：2022-05-04

Applicant: Adobe Inc.

Inventor： Haitian Zheng ,  Zhe Lin ,  Jingwan Lu ,  Scott Cohen ,  Elya Shechtman ,  Connelly Barnes ,  Jianming Zhang ,  Ning Xu ,  Sohrab Amirghodsi

IPC: G06T5/77 ,  G06T3/4046 ,  G06V10/40

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.

公开(公告)号：US12154196B2

公开(公告)日：2024-11-26

申请号：US17810392

申请日：2022-07-01

Applicant: Adobe Inc.

Inventor： Zhifei Zhang ,  Zhe Lin ,  Scott Cohen ,  Darshan Prasad ,  Zhihong Ding

IPC: G06T11/40 ,  G06T5/77 ,  G06T7/12

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

公开(公告)号：US20240169630A1

公开(公告)日：2024-05-23

申请号：US18532457

申请日：2023-12-07

Applicant: Adobe Inc.

Inventor： Soo Ye Kim ,  Zhe Lin ,  Scott Cohen ,  Jianming Zhang ,  Luis Figueroa

IPC: G06T11/60 ,  G06F3/0481 ,  G06F3/04845 ,  G06F3/0486 ,  G06T5/00 ,  G06T11/00

CPC classification number: G06T11/60 ,  G06F3/0481 ,  G06F3/04845 ,  G06F3/0486 ,  G06T5/002 ,  G06T5/005 ,  G06T11/001 ,  G06T2200/24 ,  G06T2207/20092 ,  G06T2207/20212

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to synthesize shadows for object(s). For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access an object mask of the object depicting in the digital image. The disclosed systems further combine the object mask, the digital image, and a noise representation to generate a combined representation. Moreover, the disclosed systems generate a shadow for the object from the combined representation and further generates the modified digital image by combining the shadow with the digital image.

公开(公告)号：US20240169501A1

公开(公告)日：2024-05-23

申请号：US18058601

申请日：2022-11-23

Applicant: Adobe Inc.

Inventor： Qing Liu ,  Zhe Lin ,  Luis Figueroa ,  Scott Cohen

IPC: G06T5/00 ,  G06T7/11 ,  G06T7/194

CPC classification number: G06T5/005 ,  G06T5/002 ,  G06T7/11 ,  G06T7/194 ,  G06T2200/24 ,  G06T2207/20084

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate, utilizing a segmentation neural network and without user input, object masks for objects in a digital image. The disclosed systems determine foreground and background abutting an object mask. The disclosed systems generate an expanded object mask by expanding the object mask into the foreground abutting the object mask by a first amount and expanding the object mask into the background abutting the object mask by a second amount that differs from the first amount. The disclosed systems inpaint a hole corresponding to the expanded object mask utilizing an inpainting neural network.

公开(公告)号：US20240127452A1

公开(公告)日：2024-04-18

申请号：US17937680

申请日：2022-10-03

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Haitian Zheng ,  Elya Shechtman ,  Jianming Zhang ,  Jingwan Lu ,  Ning Xu ,  Qing Liu ,  Scott Cohen ,  Sohrab Amirghodsi

IPC: G06T7/11

CPC classification number: G06T7/11 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20132

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.

公开(公告)号：US20240004924A1

公开(公告)日：2024-01-04

申请号：US17809781

申请日：2022-06-29

Applicant: Adobe Inc.

Inventor： Zhifei Zhang ,  Zhe Lin ,  Zhihong Ding ,  Scott Cohen ,  Darshan Prasad

IPC: G06F16/538 ,  G06F16/532 ,  G06T7/11 ,  G06T5/50 ,  G06F16/583

CPC classification number: G06F16/538 ,  G06F16/532 ,  G06F16/5838 ,  G06T5/50 ,  G06T7/11

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, in one or more embodiments, the disclosed systems receive an input digital image and search input and further modify the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and the disclosed systems retrieve the image search results utilizing a weighted combination of the queries. In some implementations, the disclosed systems generate an input embedding for the search input (e.g., the multi-modal search input) and retrieve the image search results using the input embedding.

公开(公告)号：US20230419571A1

公开(公告)日：2023-12-28

申请号：US17809494

申请日：2022-06-28

Applicant: Adobe Inc.

Inventor： Zhifei Zhang ,  Zhe Lin ,  Scott Cohen ,  Kevin Gary Smith

IPC: G06T11/60 ,  G06T11/20 ,  G06F16/532

CPC classification number: G06T11/60 ,  G06T11/203 ,  G06F16/532 ,  G06T2200/24 ,  G06T2207/20084 ,  G06F3/0482

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, in one or more embodiments, the disclosed systems receive an input digital image and search input and further modify the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and the disclosed systems retrieve the image search results utilizing a weighted combination of the queries. In some implementations, the disclosed systems generate an input embedding for the search input (e.g., the multi-modal search input) and retrieve the image search results using the input embedding.

公开(公告)号：US20230325992A1

公开(公告)日：2023-10-12

申请号：US17658774

申请日：2022-04-11

Applicant: Adobe Inc.

Inventor： Zhe Lin ,  Sijie Zhu ,  Jason Wen Yong Kuen ,  Scott Cohen ,  Zhifei Zhang

IPC: G06T5/50 ,  G06T3/60

CPC classification number: G06T5/50 ,  G06T3/60 ,  G06T7/194

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes artificial intelligence to learn to recommend foreground object images for use in generating composite images based on geometry and/or lighting features. For instance, in one or more embodiments, the disclosed systems transform a foreground object image corresponding to a background image using at least one of a geometry transformation or a lighting transformation. The disclosed systems further generating predicted embeddings for the background image, the foreground object image, and the transformed foreground object image within a geometry-lighting-sensitive embedding space utilizing a geometry-lighting-aware neural network. Using a loss determined from the predicted embeddings, the disclosed systems update parameters of the geometry-lighting-aware neural network. The disclosed systems further provide a variety of efficient user interfaces for generating composite digital images.