Tensor-Based Human Body Modeling

Yinpeng Chen, Zicheng Liu, Zhengyou Zhang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 105-112


In this paper, we present a novel approach to model 3D human body with variations on both human shape and pose, by exploring a tensor decomposition technique. 3D human body modeling is important for 3D reconstruction and animation of realistic human body, which can be widely used in Tele-presence and video game applications. It is challenging due to a wide range of shape variations over different people and poses. The existing SCAPE model [4] is popular in computer vision for modeling 3D human body. However, it considers shape and pose deformations separately, which is not accurate since pose deformation is persondependent. Our tensor-based model addresses this issue by jointly modeling shape and pose deformations. Experimental results demonstrate that our tensor-based model outperforms the SCAPE model quite significantly. We also apply our model to capture human body using Microsoft Kinect sensors with excellent results.

Related Material

author = {Chen, Yinpeng and Liu, Zicheng and Zhang, Zhengyou},
title = {Tensor-Based Human Body Modeling},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}