Generating synthesized digital images utilizing a multi-resolution generator neural network
US11769227B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 12, 2021 |
| Grant date | Sep 26, 2023 |
| Priority date | — |
| Expiry date | Dec 18, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V30/274
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.