A Multiview Approach for Pedestrian 3D PoseDetection and Reconstruction

ChenKaiNuaa, Xiaodong Zhao, Yujie Huang

Abstract: Aiming at the problems that most of the existing methods of constructing 3D models of human body based on 2D human body surface pose points will lead to continuous modeling jitter and local distortion of the modeling results, we propose a 3D pose point detection method based on the skinned multiplayer linear model (SMPL) in the human body, which maps the 2D pose points of the body to 3D pose points of the real scene in the multiview perspective through a clustering algorithm, and introduces Kalman filtering to de-noise the human body pose points. The Kalman filter is introduced to denoise the human body posture points. In the process of constructing a 3D model of the human body based on 3D pose points, we construct an end-to-end human 3D modeling network (SMPL-VAE) based on the correction of gradient descent regression network by the automatic variational approach (VAE), which is more in line with the local modeling of the human body’s motion structure while maintaining the overall proportion.The results on open dataset Shelf show that our methods improve the quality of human post point detection and modeling.