Learning Basis Representation to Refine 3D Human Pose Estimations

Abstract

Estimating 3D human poses from 2D joint positions is an ill-posed problem, and is further complicated by the fact that the estimated 2D joints usually have errors to which most of the 3D pose estimators are sensitive. In this work, we present an approach to refine inaccurate 3D pose estimations. The core idea of the approach is to learn a number of bases to obtain tight approximations of the low-dimensional pose manifold where a 3D pose is represented by a convex combination of the bases.

Publication
In Thirty-third AAAI Conference on Artificial Intelligence
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