3D human body pose estimation using a model trained from unlabeled multi-view data
US11417011B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 9, 2020 |
| Grant date | Aug 16, 2022 |
| Priority date | — |
| Expiry date | Jan 15, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30196
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Learning to estimate a 3D body pose, and likewise the pose of any type of object, from a single 2D image is of great interest for many practical graphics applications and generally relies on neural networks that have been trained with sample data which annotates (labels) each sample 2D image with a known 3D pose. Requiring this labeled training data however has various drawbacks, including for example that traditionally used training data sets lack diversity and therefore limit the extent to which neural networks are able to estimate 3D pose. Expanding these training data sets is also difficult since it requires manually provided annotations for 2D images, which is time consuming and prone to errors. The present disclosure overcomes these and other limitations of existing techniques by providing a model that is trained from unlabeled multi-view data for use in 3D pose estimation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.