System for training neural networks that predict the parameters of a human mesh model using dense depth and part-based UV map annotations
US12205311B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 23, 2023 |
| Grant date | Jan 21, 2025 |
| Priority date | — |
| Expiry date | Oct 23, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30201
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system for training neural networks that predict the parameters of a human mesh model is disclosed herein. The system includes at least one camera and a data processor configured to execute computer executable instructions for: receiving a first frame and a second frame of a video from the at least one camera; extracting first and second image data from the first and second frames of the video; inputting the sequence of frames of the video into a human mesh estimator module, the human mesh estimator module estimating mesh parameters from the sequence of frames of the video so as to determine a predicted mesh; and generating a training signal for input into the human mesh estimator module by using a two-dimensional keypoint loss module that compares a first set of two-dimensional image-based keypoints to a second set of two-dimensional model-based keypoints.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.