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

Abstract

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.