research-article

A 3D human motion refinement method based on sparse motion bases selection

Authors:

Jian J. ZhangAuthors Info & Claims

CASA '16: Proceedings of the 29th International Conference on Computer Animation and Social Agents

Pages 53 - 60

https://doi.org/10.1145/2915926.2915943

Published: 23 May 2016 Publication History

Abstract

Motion capture (MOCAP) is an important technique that is widely used in many areas such as computer animation, film industry, physical training and so on. Even with professional MOCAP system, the missing marker problems always occur. Motion refinement is an essential preprocessing step for MOCAP data based applications. Although many existing approaches for motion refinement have been developed, it is still a challenging task due to the complexity and diversity of human motion. A data driven based motion refinement method is proposed in this paper, which modifies the traditional sparse coding process for special task of motion recovery from missing parts. Meanwhile, the objective function is derived by taking both statistical and kinematical property of motion data into account. Poselet model and moving window grouping are applied in the proposed method to achieve a fine-grained feature representation, which preserves the embedded spatial-temporal kinematic information. 5 motion dictionaries are learnt for each kind of poselet from training data in parallel. The motion refine problem is finally solved as an ℓ1-minimization problem. Compared with several state-of-art motion refine methods, the experimental result shows that our approach outperforms the competitors.

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Cited By

Raj MGeorge S(2024)A Fast and Efficient Approach for Human Action Recovery From Corrupted 3-D Motion Capture Data Using QR Decomposition-Based Approximate SVDIEEE Transactions on Human-Machine Systems10.1109/THMS.2024.340029054:4(395-405)Online publication date: Aug-2024
https://doi.org/10.1109/THMS.2024.3400290
Yasin HGhani SKrüger B(2023)An Effective and Efficient Approach for 3D Recovery of Human Motion Capture DataSensors10.3390/s2307366423:7(3664)Online publication date: 31-Mar-2023
https://doi.org/10.3390/s23073664
Xia GXue PSun HSun YZhang DLiu Q(2022)Local Self-Expression Subspace Learning Network for Motion Capture DataIEEE Transactions on Image Processing10.1109/TIP.2022.318982231(4869-4883)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3189822
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Published In

cover image ACM Other conferences

CASA '16: Proceedings of the 29th International Conference on Computer Animation and Social Agents

May 2016

200 pages

ISBN:9781450347457

DOI:10.1145/2915926

Conference Chairs:
Nadia Magnenat Thalmann
MIRALab, University of Geneva and NTU Singapore
,
Marina L. Gavrilova
University of Calgary, Canada
,
Program Chairs:
Taku Komura
Edingburgh University, UK
,
Frederic Cordier
University of Haute-Alsace, France
,
Daniel Thalmann
NTU, Singapore & EPFL, Switzerland

Copyright © 2016 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 May 2016

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Research-article
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Refereed limited

Funding Sources

National Natural Science Foundation of China
Santander PGR Mobility Award
EU project AniNex
National Natural Science Foundation of China
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Conference

CASA '16

CASA '16: Computer Animation and Social Agents

May 23 - 25, 2016

Geneva, Switzerland

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Overall Acceptance Rate 18 of 110 submissions, 16%

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Cited By

Raj MGeorge S(2024)A Fast and Efficient Approach for Human Action Recovery From Corrupted 3-D Motion Capture Data Using QR Decomposition-Based Approximate SVDIEEE Transactions on Human-Machine Systems10.1109/THMS.2024.340029054:4(395-405)Online publication date: Aug-2024
https://doi.org/10.1109/THMS.2024.3400290
Yasin HGhani SKrüger B(2023)An Effective and Efficient Approach for 3D Recovery of Human Motion Capture DataSensors10.3390/s2307366423:7(3664)Online publication date: 31-Mar-2023
https://doi.org/10.3390/s23073664
Xia GXue PSun HSun YZhang DLiu Q(2022)Local Self-Expression Subspace Learning Network for Motion Capture DataIEEE Transactions on Image Processing10.1109/TIP.2022.318982231(4869-4883)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3189822
Nakatsuka CKomorita S(2021)Denoising 3D Human Poses from Low-Resolution Video using Variational Autoencoder2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS51168.2021.9636144(4625-4630)Online publication date: 27-Sep-2021
https://doi.org/10.1109/IROS51168.2021.9636144
Li JXiao DLi KLi J(2021)Graph Matching for Marker Labeling and Missing Marker Reconstruction With Bone Constraint by LSTM in Optical Motion CaptureIEEE Access10.1109/ACCESS.2021.30603859(34868-34881)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3060385
Kamali KAkbari ADesrosiers CAkbarzadeh AOtis MAyena J(2020)Low-Rank and Sparse Recovery of Human Gait DataSensors10.3390/s2016452520:16(4525)Online publication date: 13-Aug-2020
https://doi.org/10.3390/s20164525
Li RSi WWeinmann MKlein R(2019)Constraint-Based Optimized Human Skeleton Extraction from Single-Depth CameraSensors10.3390/s1911260419:11(2604)Online publication date: 7-Jun-2019
https://doi.org/10.3390/s19112604
Imamura ROkuda M(2018)MOCAP signal interpolation using low-rank matrix recovery2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)10.23919/APSIPA.2018.8659679(871-874)Online publication date: Nov-2018
https://doi.org/10.23919/APSIPA.2018.8659679
Xia GSun HChen BLiu QFeng LZhang GHang R(2018)Nonlinear Low-Rank Matrix Completion for Human Motion RecoveryIEEE Transactions on Image Processing10.1109/TIP.2018.281210027:6(3011-3024)Online publication date: Jun-2018
https://doi.org/10.1109/TIP.2018.2812100
Hu WWang ZLiu SYang XYu GZhang J(2018)Motion Capture Data Completion via Truncated Nuclear Norm RegularizationIEEE Signal Processing Letters10.1109/LSP.2017.268704425:2(258-262)Online publication date: Feb-2018
https://doi.org/10.1109/LSP.2017.2687044
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