Systems and methods for training models to predict dense correspondences in images using geodesic distances
US11954899B2 · kind B2 · utility
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Key dates
| Filing date | Mar 11, 2021 |
| Grant date | Apr 9, 2024 |
| Priority date | — |
| Expiry date | Mar 11, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30196
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for training models to predict dense correspondences across images such as human images. A model may be trained using synthetic training data created from one or more 3D computer models of a subject. In addition, one or more geodesic distances derived from the surfaces of one or more of the 3D models may be used to generate one or more loss values, which may in turn be used in modifying the model's parameters during training.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.