Abstract

This paper addresses the challenge of 3D full-body hu-man pose estimation from a monocular image sequence.Here, two cases are considered: (i) the image locations ofthe human joints are provided and (ii) the image locationsof joints are unknown. In the former case, a novel approachis introduced that integrates a sparsity-driven 3D geometricprior and temporal smoothness. In the latter case, the for-mer case is extended by treating the image locations of thejoints as latent variables to take into account considerable uncertainties in 2D joint locations. A deep fully convolu-tional network is trained to predict the uncertainty mapsof the 2D joint locations. The 3D pose estimates are real-ized via an Expectation-Maximization algorithm over the entire sequence, where it is shown that the 2D joint location uncertainties can be conveniently marginalized out during inference. Empirical evaluation on the Human3.6M dataset shows that the proposed approaches achieve greater 3D pose estimation accuracy over state-of-the-art base-lines. Further, the proposed approach outperforms a publicly available 2D pose estimation baseline on the challenging PennAction dataset.