Discrete subspace structure constrained human motion capture data recovery

Recently, low-rank matrix completion (LRMC) has shown a promising performance in human motion capture (mocap) data recovery, which assumes that the input data is low-rank. However, for most mocap data comprising multiple different activities, only using LRMC to restore the data may result in erroneous results, since the low-rank assumption is not necessarily satisfied. The current method solves this problem in a two-stage manner by combining the LRMC and subspace clustering. But it ignores the fact that the processes of recovering and clustering depend on each other, and so is sub-optimal. In this paper, we propose a novel discrete subspace structure (DSS) constrained approach to recover human mocap data, which jointly optimizing the tasks of subspace clustering and the LRMC. The proposed DSS algorithm learns different subspaces' indicators such that the mocap recovery problem is divided into several LRMC subproblems, where each matrix of the points drawn from a single subspace is low-rank. As a byproduct, we can obtain the temporal clustering results of mocap data. To better approximate the low-rank property, we utilize the tth power of Schatten p-norm to approximate the rank instead of the nuclear norm. Moreover, we add two regularization terms to take care of the noise effect and temporal stability of mocap data. The obtained model comes down to a non-convex optimization problem, which we solve tactically and efficiently by employing the alternating direction method of multipliers (ADMM). Experiments on the CMU dataset and the HDM05 dataset validate the effectiveness of the proposed algorithm in both mocap data recovery and temporal subspace clustering. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a novel discrete subspace structure (DSS) constrained approach is proposed for recovering human mocap data by jointly optimizing the tasks of subspace clustering and low-rank matrix completion (LRMC). Experimental results validate the effectiveness of the proposed algorithm in both mocap data recovery and temporal subspace clustering.