Human pose extraction from monocular videos using constrained non-rigid factorization

Abstract

We focus on the problem of automatically extracting the 3D configuration of human poses from 2D image features tracked over a finite interval of time . This problem is highly non-linear in nature and confounds standard regression techniques. Our approach effectively marries a non-rigid factorization algorithm with prior learned statistical models from archival motion capture database. We show that a stand alone non-rigid factorization algorithm is highly unsuitable for this problem. However, when coupled with the learned statistical model in the form of a constrained non- linear programming method, it yields a substantially better solution.