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Towards Robust RGB-D Human Mesh Recovery
- Publication Year :
- 2019
-
Abstract
- We consider the problem of human pose estimation. While much recent work has focused on the RGB domain, these techniques are inherently under-constrained since there can be many 3D configurations that explain the same 2D projection. To this end, we propose a new method that uses RGB-D data to estimate a parametric human mesh model. Our key innovations include (a) the design of a new dynamic data fusion module that facilitates learning with a combination of RGB-only and RGB-D datasets, (b) a new constraint generator module that provides SMPL supervisory signals when explicit SMPL annotations are not available, and (c) the design of a new depth ranking learning objective, all of which enable principled model training with RGB-D data. We conduct extensive experiments on a variety of RGB-D datasets to demonstrate efficacy.<br />Comment: 10 pages, 4 figures, 4 tables
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1911.07383
- Document Type :
- Working Paper