Invention Grant
- Patent Title: Generating synthesized digital images utilizing a multi-resolution generator neural network
-
Application No.: US17400426Application Date: 2021-08-12
-
Publication No.: US11769227B2Publication Date: 2023-09-26
- 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: G06K9/00
- IPC: G06K9/00 ; G06T3/40 ; G06N3/04 ; G06V10/40 ; G06V30/262 ; G06F18/25

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.
Public/Granted literature
- US20230053588A1 GENERATING SYNTHESIZED DIGITAL IMAGES UTILIZING A MULTI-RESOLUTION GENERATOR NEURAL NETWORK Public/Granted day:2023-02-23
Information query