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Motion Capture Data Completion via Truncated Nuclear Norm Regularization.

Hu, W., Wang, Z., Liu, S., Yang, X., Yu, G. and Zhang, J. J., 2018. Motion Capture Data Completion via Truncated Nuclear Norm Regularization. IEEE Signal Processing Letters, 25 (2), 258 - 262.

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2017_SPL_MOCAP Data Completion via TrNN.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.


DOI: 10.1109/LSP.2017.2687044


The objective of motion capture (mocap) data completion is to recover missing measurement of the body markers from mocap. It becomes increasingly challenging as the missing ratio and duration of mocap data grow. Traditional approaches usually recast this problem as a low-rank matrix approximation problem based on the nuclear norm. However, the nuclear norm defined as the sum of all the singular values of a matrix is not a good approximation to the rank of mocap data. This paper proposes a novel approach to solve mocap data completion problem by adopting a new matrix norm, called truncated nuclear norm. An efficient iterative algorithm is designed to solve this problem based on the augmented Lagrange multiplier. The convergence of the proposed method is proved mathematically under mild conditions. To demonstrate the effectiveness of the proposed method, various comparative experiments are performed on synthetic data and mocap data. Compared to other methods, the proposed method is more efficient and accurate.

Item Type:Article
Uncontrolled Keywords:Augmented Lagrange multiplier (ALM); low rank; motion capture (mocap); truncated nuclear norm (TrNN)
Group:Faculty of Media & Communication
ID Code:30346
Deposited By: Symplectic RT2
Deposited On:07 Feb 2018 15:43
Last Modified:14 Mar 2022 14:09


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