Invention Grant
- Patent Title: Generating synthesized digital images utilizing class-specific machine-learning models
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Application No.: US17400474Application Date: 2021-08-12
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Publication No.: US11861762B2Publication Date: 2024-01-02
- Inventor: Yuheng Li , Yijun Li , Jingwan Lu , Elya Shechtman , Krishna Kumar Singh
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: Keller Preece PLLC
- Main IPC: G06T11/00
- 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.
Public/Granted literature
- US20230051749A1 GENERATING SYNTHESIZED DIGITAL IMAGES UTILIZING CLASS-SPECIFIC MACHINE-LEARNING MODELS Public/Granted day:2023-02-16
Information query
IPC分类:
G | 物理 |
G06 | 计算;推算或计数 |
G06T | 一般的图像数据处理或产生 |
G06T11/00 | 2D〔二维〕图像的生成 |