Restoring corrupted motion capture data via jointly low-rank matrix completion

Motion capture (mocap) technology is widely used in various applications. The acquired mocap data usually has missing data due to occlusions or ambiguities. Therefore, restoring the missing entries of the mocap data is a fundamental issue in mocap data analysis. Based on jointly low-rank matrix completion, this paper presents a practical and highly efficient algorithm for restoring the missing mocap data. Taking advantage of the unique properties of mocap data (i.e, strong correlation among the data), we represent the corrupted data as two types of matrices, where both the local and global characteristics are taken into consideration. Then we formulate the problem as a convex optimization problem, where the missing data is recovered by solving the two matrices using the alternating direction method of multipliers algorithm. Experimental results demonstrate that the proposed scheme significantly outperforms the state-of-the-art algorithms in terms of both the quality and computational cost.