公开(公告)号：US11861762B2

公开(公告)日：2024-01-02

申请号：US17400474

申请日：2021-08-12

Applicant: Adobe Inc.

Inventor： Yuheng Li ,  Yijun Li ,  Jingwan Lu ,  Elya Shechtman ,  Krishna Kumar Singh

IPC: G06T11/00

CPC classification number: G06T11/00 ,  G06T2210/12

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images using class-specific generators for objects of different classes. The disclosed system modifies a synthesized digital image by utilizing a plurality of class-specific generator neural networks to generate a plurality of synthesized objects according to object classes identified in the synthesized digital image. The disclosed system determines object classes in the synthesized digital image such as via a semantic label map corresponding to the synthesized digital image. The disclosed system selects class-specific generator neural networks corresponding to the classes of objects in the synthesized digital image. The disclosed system also generates a plurality of synthesized objects utilizing the class-specific generator neural networks based on contextual data associated with the identified objects. The disclosed system generates a modified synthesized digital image by replacing the identified objects in the synthesized digital images with the synthesized objects.

公开(公告)号：US20230051749A1

公开(公告)日：2023-02-16

申请号：US17400474

申请日：2021-08-12

Applicant: Adobe Inc.

Inventor： Yuheng Li ,  Yijun Li ,  Jingwan Lu ,  Elya Shechtman ,  Krishna Kumar Singh

IPC: G06T11/00

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images using class-specific generators for objects of different classes. The disclosed system modifies a synthesized digital image by utilizing a plurality of class-specific generator neural networks to generate a plurality of synthesized objects according to object classes identified in the synthesized digital image. The disclosed system determines object classes in the synthesized digital image such as via a semantic label map corresponding to the synthesized digital image. The disclosed system selects class-specific generator neural networks corresponding to the classes of objects in the synthesized digital image. The disclosed system also generates a plurality of synthesized objects utilizing the class-specific generator neural networks based on contextual data associated with the identified objects. The disclosed system generates a modified synthesized digital image by replacing the identified objects in the synthesized digital images with the synthesized objects.

公开(公告)号：US11769227B2

公开(公告)日：2023-09-26

申请号：US17400426

申请日：2021-08-12

Applicant: Adobe Inc.

Inventor： Yuheng Li ,  Yijun Li ,  Jingwan Lu ,  Elya Shechtman ,  Krishna Kumar Singh

IPC: G06K9/00 ,  G06T3/40 ,  G06N3/04 ,  G06V10/40 ,  G06V30/262 ,  G06F18/25

CPC classification number: G06T3/4046 ,  G06F18/253 ,  G06N3/04 ,  G06V10/40 ,  G06V30/274

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images via multi-resolution generator neural networks. The disclosed system extracts multi-resolution features from a scene representation to condition a spatial feature tensor and a latent code to modulate an output of a generator neural network. For example, the disclosed systems utilizes a base encoder of the generator neural network to generate a feature set from a semantic label map of a scene. The disclosed system then utilizes a bottom-up encoder to extract multi-resolution features and generate a latent code from the feature set. Furthermore, the disclosed system determines a spatial feature tensor by utilizing a top-down encoder to up-sample and aggregate the multi-resolution features. The disclosed system then utilizes a decoder to generate a synthesized digital image based on the spatial feature tensor and the latent code.

公开(公告)号：US20230342884A1

公开(公告)日：2023-10-26

申请号：US17725818

申请日：2022-04-21

Applicant: Adobe Inc.

Inventor： Krishna Kumar Singh ,  Yuheng Li ,  Yijun Li ,  Jingwan Lu ,  Elya Shechtman

IPC: G06T5/00 ,  G06V10/82 ,  G06V10/74 ,  G06N3/04

CPC classification number: G06T5/002 ,  G06V10/82 ,  G06V10/761 ,  G06N3/0454 ,  G06T2207/20081

Abstract: An image inpainting system is described that receives an input image that includes a masked region. From the input image, the image inpainting system generates a synthesized image that depicts an object in the masked region by selecting a first code that represents a known factor characterizing a visual appearance of the object and a second code that represents an unknown factor characterizing the visual appearance of the object apart from the known factor in latent space. The input image, the first code, and the second code are provided as input to a generative adversarial network that is trained to generate the synthesized image using contrastive losses. Different synthesized images are generated from the same input image using different combinations of first and second codes, and the synthesized images are output for display.

公开(公告)号：US20230053588A1

公开(公告)日：2023-02-23

申请号：US17400426

申请日：2021-08-12

Applicant: Adobe Inc.

Inventor： Yuheng Li ,  Yijun Li ,  Jingwan Lu ,  Elya Shechtman ,  Krishna Kumar Singh

IPC: G06T3/40 ,  G06K9/46 ,  G06K9/62 ,  G06K9/72 ,  G06N3/04

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images via multi-resolution generator neural networks. The disclosed system extracts multi-resolution features from a scene representation to condition a spatial feature tensor and a latent code to modulate an output of a generator neural network. For example, the disclosed systems utilizes a base encoder of the generator neural network to generate a feature set from a semantic label map of a scene. The disclosed system then utilizes a bottom-up encoder to extract multi-resolution features and generate a latent code from the feature set. Furthermore, the disclosed system determines a spatial feature tensor by utilizing a top-down encoder to up-sample and aggregate the multi-resolution features. The disclosed system then utilizes a decoder to generate a synthesized digital image based on the spatial feature tensor and the latent code.