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

Feng, Yinfu; Xiao, Jun; Zhuang, Yueting; Yang, Xiaosong; Zhang, Jian Jun; Song, Rong

doi:10.1016/j.ins.2014.03.013

Cited by 65 publications

(40 citation statements)

References 31 publications

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“…They reformulate the human motion refinement into a low-rank matrix optimization where singular value thresholding (SVT) is applied to solve the objective function. After that, Feng et al [22] have proposed a motion data refinement via a matrix completion method using both the low-rank structure and temporal stability properties of the motion data. Liu et al [23] have presented a MOCAP data denoising approach via filtered subspace clustering and low rank matrix approximation.…”

Section: Matrix Completion Methodsmentioning

confidence: 99%

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Human motion data refinement unitizing structural sparsity and spatial-temporal information

Wang

Liu

Qian

et al. 2016

2016 IEEE 13th International Conference on Signal Processing (ICSP)

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Abstract-Human motion capture techniques (MOCAP) are widely applied in many areas such as computer vision, computer animation, digital effect and virtual reality. Even with professional MOCAP system, the acquired motion data still always contains noise and outliers, which highlights the need for the essential motion refinement methods. In recent years, many approaches for motion refinement have been developed, including signal processing based methods, sparse coding based methods and low-rank matrix completion based methods. However, motion refinement is still a challenging task due to the complexity and diversity of human motion. In this paper, we propose a data-driven-based human motion refinement approach by exploiting the structural sparsity and spatio-temporal information embedded in motion data. First of all, a human partial model is applied to replace the entire pose model for a better feature representation to exploit the abundant local body posture. Then, a dictionary learning which is for special task of motion refinement is designed and applied in parallel. Meanwhile, the objective function is derived by taking the statistical and locality property of motion data into account. Compared with several state-of-art motion refine methods, the experimental result demonstrates that our approach outperforms the competitors.

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Section: Matrix Completion Methodsmentioning

confidence: 99%

“…The detail of TDD implementation is omitted due to paragraph limitation, which is available in [22].…”

Section: Motion Recovery 1) Trust Data Detectionmentioning

confidence: 99%

Human motion data refinement unitizing structural sparsity and spatial-temporal information

Wang

Liu

Qian

et al. 2016

2016 IEEE 13th International Conference on Signal Processing (ICSP)

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“…The main idea of these methods is to exploit the low-rank property of motion matrix to remove noises and estimate the missing markers [2], [5], [10], [11], [12], [13]. Theoretically, Candès and Recht [14] proved that a low-rank matrix can be accurately recovered from the observations of a small fraction of its entries by solving a nuclear norm minimization problem.…”

mentioning

confidence: 99%

“…Based on [10], Feng et al [12] additionally took the temporal stability and noise effect of mocap data into account, obtaining a model as follow:…”

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confidence: 99%

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Motion Capture Data Completion via Truncated Nuclear Norm Regularization

Wang

Liu

et al. 2018

IEEE Signal Process. Lett.

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Abstract-The objective of mocap data completion is to recover missing measurement of the body markers from motion capture. 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 (TrNN). 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.

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