3D human body reconstruction based on SMPL model

Recovering 3D human pose and body shape from a monocular image is a challenging task in computer vision. In this paper, we present an optimization-based algorithm and an innovative framework to reconstruct 3D human body from a single monocular image. All the evaluation tasks are established on the basis of the classic parametric 3D body model SMPL. Firstly, a new combined objective function of SMPL parameters is proposed to involve four loss terms on 2D joints, 3D joints, facial landmarks and pose priori, respectively, which increase the reliability of the evaluation results dramatically. Furthermore, we use the estimation results given by an end-to-end regression network as the initial values of the parameters, which has been proved to speed up the optimization process. The experiments on benchmark datasets Human 3.6 M, LSP and a wild dataset demonstrate that our model achieves an accurate and robust estimation of the 3D human body and outperforms the popular competing algorithms in precision and robustness.