Utilizing a neural network having a two-stream encoder architecture to generate composite digital images
US11158055B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 26, 2019 |
| Grant date | Oct 26, 2021 |
| Priority date | — |
| Expiry date | Dec 2, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T11/60
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to utilizing a neural network having a two-stream encoder architecture to accurately generate composite digital images that realistically portray a foreground object from one digital image against a scene from another digital image. For example, the disclosed systems can utilize a foreground encoder of the neural network to identify features from a foreground image and further utilize a background encoder to identify features from a background image. The disclosed systems can then utilize a decoder to fuse the features together and generate a composite digital image. The disclosed systems can train the neural network utilizing an easy-to-hard data augmentation scheme implemented via self-teaching. The disclosed systems can further incorporate the neural network within an end-to-end framework for automation of the image composition process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.