3-D motion recovery via low rank matrix restoration on articulation graphs

This paper addresses the challenge of 3-D skeleton recovery by exploiting the spatio-temporal correlations of corrupted 3D skeleton sequences. A skeleton sequence is represented as a matrix. We propose a novel low-rank solution that effectively integrates both a low-rank model for robust skeleton recovery based on temporal coherence, and an articulation-graph-based isometric constraint for spatial coherence, namely consistency of bone lengths. The proposed model is formulated as a constrained optimization problem, which is efficiently solved by the Augmented Lagrangian Method with a Gauss-Newton solver for the subproblem of isometric optimization. Experimental results on the CMU motion capture dataset and a Kinect dataset show that the proposed approach achieves better recovery accuracy over a state-of-the-art method. The proposed method has wide applicability for skeleton tracking devices, such as the Kinect, because these devices cannot provide accurate reconstructions of complex motions, especially in the presence of occlusion.