SDM3d: shape decomposition of multiple geometric priors for 3D pose estimation

Recovering the 3D human pose from a single image with 2D joints is a challenging task in computer vision applications. The sparse representation (SR) model has been successfully adopted in 3D pose estimation approaches. However, since existing available training 3D data are often collected in a constrained environment (i.e., indoor) with limited diversity of subjects and actions, most SR-based approaches would have a lower generalization to real-world scenarios that may contain more complex cases. To alleviate this issue, this paper proposes SDM3d, a novel shape decomposition using multiple geometric priors for 3D pose estimation. SDM3d makes a new attempt by separating a 3D pose into the global structure and body deformations that are encoded explicitly via different priors constraints. Furthermore, a joint learning strategy is designed to learn two over-complete dictionaries from training data to capture more geometric priors information. We have evaluated SDM3d on four well-recognized benchmarks, i.e., Human3.6M, HumanEva-I, CMU MoCap, and MPII. The experiment results show the effectiveness of SDM3d.