Generator, generator training method, and method for avoiding image coordinate adhesion
US12056903B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 29, 2023 |
| Grant date | Aug 6, 2024 |
| Priority date | — |
| Expiry date | Jun 29, 2043 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02D10/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Disclosed are a gated network-based generator, a generator training method, and a method for avoiding image coordinate adhesion. The generator processes, by using an image input layer, a to-be-processed image as an image sequence and inputs it to a feature encoding layer. Multiple feature encoding layers encode the image sequence by using a gated convolutional network, to obtain an image code. Moreover, multiple image decoding layers decode the image code by using an inverse gated convolution unit, to obtain a target image sequence. Finally, an image output layer splices the target image sequence to obtain a target image. Therefore, a character feature in the obtained target image is more obvious, making details of a facial image of generated digital human more vivid, whereby solving a problem of image coordinate adhesion in a digital human image generated by an existing generator using a generative adversarial network, and improving user experience.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.