JSL3d: Joint subspace learning with implicit structure supervision for 3D pose estimation

Abstract

Estimating 3D human poses from a single image is an important task in computer graphics. Most model-based estimation methods represent the labeled/detected 2D poses and the projection of approximated 3D poses using vector representations of body joints. However, such lower-dimensional vector representations fail to maintain the spatial relations of original body joints, because the representations do not consider the inherent structure of body joints. In this paper, we propose JSL3d, a novel joint subspace learning approach with implicit structure supervision based on Sparse Representation (SR) model, capturing the latent spatial relations of 2D body joints by an end-to-end autoencoder network. JSL3djointly combines the learned latent spatial relations and 2D joints as inputs for the standard SR inference frame. The optimization is simultaneously processed via geometric priors in both latent and original feature spaces. We have evaluated JSL3dusing four large-scale and well-recognized benchmarks, including Human3.6M, HumanEva-I, CMU MoCap and MPII. The experiment results demonstrate the effectiveness of JSL3d.