Learning dense correspondences for images
US12169882B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 1, 2022 |
| Grant date | Dec 17, 2024 |
| Priority date | — |
| Expiry date | May 4, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T3/18
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present disclosure relate to learning dense correspondences for images. Systems and methods are disclosed that disentangle structure and texture (or style) representations of GAN synthesized images by learning a dense pixel-level correspondence map for each image during image synthesis. A canonical coordinate frame is defined and a structure latent code for each generated image is warped to align with the canonical coordinate frame. In sum, the structure associated with the latent code is mapped into a shared coordinate space (canonical coordinate space), thereby establishing correspondences in the shared coordinate space. A correspondence generation system receives the warped coordinate correspondences as an encoded image structure. The encoded image structure and a texture latent code are used to synthesize an image. The shared coordinate space enables propagation of semantic labels from reference images to synthesized images.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.