Exploiting temporal stability and low-rank structure for motion capture data refinement.

Feng, Y., Xiao, J., Zhuang, Y., Song, R., Yang, X. and Zhang, J. J., 2014. Exploiting temporal stability and low-rank structure for motion capture data refinement. Information Sciences, 277, 777 - 793 .

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Exploiting_temporal_stability_and_low-rank_structure_for_motion_capture_data_refinement.pdf - Published Version


DOI: 10.1016/j.ins.2014.03.013


Inspired by the development of the matrix completion theories and algorithms, a low-rank based motion capture (mocap) data refinement method has been developed, which has achieved encouraging results. However, it does not guarantee a stable outcome if we only consider the low-rank property of the motion data. To solve this problem, we propose to exploit the temporal stability of human motion and convert the mocap data refinement problem into a robust matrix completion problem, where both the low-rank structure and temporal stability properties of the mocap data as well as the noise effect are considered. An efficient optimization method derived from the augmented Lagrange multiplier algorithm is presented to solve the proposed model. Besides, a trust data detection method is also introduced to improve the degree of automation for processing the entire set of the data and boost the performance. Extensive experiments and comparisons with other methods demonstrate the effectiveness of our approaches on both predicting missing data and de-noising. © 2014 Elsevier Inc. All rights reserved.

Item Type:Article
Group:Media School
ID Code:21397
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:01 Sep 2014 10:43
Last Modified:01 Sep 2014 10:43


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