Human Model Pose and Shape Reconstruction

In order to avoid pose inversion in human reconstruction from a single image,this paper presents an approach to automatically reconstruct a 3D human pose sequence from a video based on adjacent frame pose constraint and human contour matching.Given a video frame,it firstly estimates 2D joint positions,and extracts facial feature points as well as 2D contour lines of the character.Then it fits the SMPL,a statistical body shape model,to the aforementioned 2D information by minimizing an energy function.Finally,the approach reconstructs a sequence of 3D mesh models similar to the poses and shapes of the character in the video.The algorithm has been tested on videos from MPI-INF-3DHP datasets and Youku and Selfies.The experimental results show that the proposed method can effectively avoid pose inversion,reconstruct correct head postures and obtain better body shapes compared to the SMPLify approach and other existing methods.