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Mathematical Problems in Engineering
Volume 2013, Article ID 715808, 8 pages
Research Article

Optimization and Soft Constraints for Human Shape and Pose Estimation Based on a 3D Morphable Model

1Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
2Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing 100044, China
3College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China

Received 10 April 2013; Revised 9 August 2013; Accepted 9 August 2013

Academic Editor: Yang Xu

Copyright © 2013 Dianyong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose an approach about multiview markerless motion capture based on a 3D morphable human model. This morphable model was learned from a database of registered 3D body scans in different shapes and poses. We implement pose variation of body shape by the defined underlying skeleton. At the initialization step, we adapt the 3D morphable model to the multi-view images by changing its shape and pose parameters. Then, for the tracking step, we implement a method of combining the local and global algorithm to do the pose estimation and surface tracking. And we add the human pose prior information as a soft constraint to the energy of a particle. When it meets an error after the local algorithm, we can fix the error using less particles and iterations. We demonstrate the improvements with estimating result from a multi-view image sequence.