Generating stylized-stroke images from source images utilizing style-transfer-neural networks with non-photorealistic-rendering
US10748324B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Nov 8, 2018 |
| Grant date | Aug 18, 2020 |
| Priority date | — |
| Expiry date | Nov 22, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2200/24
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
This disclosure relates to methods, non-transitory computer readable media, and systems that integrate (or embed) a non-photorealistic rendering (“NPR”) generator with a style-transfer-neural network to generate stylized images that both correspond to a source image and resemble a stroke style. By integrating an NPR generator with a style-transfer-neural network, the disclosed methods, non-transitory computer readable media, and systems can accurately capture a stroke style resembling one or both of stylized edges or stylized shadings. When training such a style-transfer-neural network, the integrated NPR generator can enable the disclosed methods, non-transitory computer readable media, and systems to use real-stroke drawings (instead of conventional paired-ground-truth drawings) for training the network to accurately portray a stroke style. In some implementations, the disclosed methods, non-transitory computer readable media, and systems can either train or apply a style-transfer-neural network that captures a variety of stroke styles, such as different edge-stroke styles or shading-stroke styles.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.