  This paper presents a method which can track and 3D reconstruct the non-rigid
surface motion of human performance using a moving RGB-D camera. 3D
reconstruction of marker-less human performance is a challenging problem due to
the large range of articulated motions and considerable non-rigid deformations.
Current approaches use local optimization for tracking. These methods need many
iterations to converge and may get stuck in local minima during sudden
articulated movements. We propose a puppet model-based tracking approach using
skeleton prior, which provides a better initialization for tracking articulated
movements. The proposed approach uses an aligned puppet model to estimate
correct correspondences for human performance capture. We also contribute a
synthetic dataset which provides ground truth locations for frame-by-frame
geometry and skeleton joints of human subjects. Experimental results show that
our approach is more robust when faced with sudden articulated motions, and
provides better 3D reconstruction compared to the existing state-of-the-art
approaches.
