Motion capture data recovery using skeleton constrained singular value thresholding
Motion capture data could be missing due to imperfections during the acquisition process. Singular Value Thresholding is an effective method to recover missing motion capture data. However, its effectiveness decreases significantly when markers are missing for longer periods of time. To alleviat...
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sg-ntu-dr.10356-1034602020-03-07T14:00:34Z Motion capture data recovery using skeleton constrained singular value thresholding Tan, Cheen-Hau Hou, JunHui Chau, Lap-Pui School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Data Motion capture data could be missing due to imperfections during the acquisition process. Singular Value Thresholding is an effective method to recover missing motion capture data. However, its effectiveness decreases significantly when markers are missing for longer periods of time. To alleviate this problem, we utilize the fact that human bones are rigid to constrain inter-marker distances of specific sets of markers. We extend the Singular Value Thresholding method for mocap recovery to include skeleton constraints. On average, our proposed method improves on the Singular Value Thresholding method by 40%, and performs 4% better than a recent state of the art method at up to 11 times faster computation time. Accepted version 2014-12-19T07:42:19Z 2019-12-06T21:13:11Z 2014-12-19T07:42:19Z 2019-12-06T21:13:11Z 2014 2014 Journal Article Tan, C.-H., Hou, J., & Chau, L.-P. Motion capture data recovery using skeleton constrained singular value thresholding. The visual computer. https://hdl.handle.net/10356/103460 http://hdl.handle.net/10220/24494 10.1007/s00371-014-1031-5 en The visual computer © 2014 Springer-Verlag Berlin Heidelberg. This is the author created version of a work that has been peer reviewed and accepted for publication by The Visual Computer, Springer-Verlag Berlin Heidelberg. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [Article DOI: http://dx.doi.org/10.1007/s00371-014-1031-5]. 10 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Data Tan, Cheen-Hau Hou, JunHui Chau, Lap-Pui Motion capture data recovery using skeleton constrained singular value thresholding |
description |
Motion capture data could be missing due to imperfections
during the acquisition process. Singular Value Thresholding
is an effective method to recover missing motion capture data.
However, its effectiveness decreases significantly when markers
are missing for longer periods of time. To alleviate this problem,
we utilize the fact that human bones are rigid to constrain
inter-marker distances of specific sets of markers. We extend
the Singular Value Thresholding method for mocap recovery to
include skeleton constraints. On average, our proposed method
improves on the Singular Value Thresholding method by 40%,
and performs 4% better than a recent state of the art method
at up to 11 times faster computation time. |
author2 |
School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Tan, Cheen-Hau Hou, JunHui Chau, Lap-Pui |
format |
Article |
author |
Tan, Cheen-Hau Hou, JunHui Chau, Lap-Pui |
author_sort |
Tan, Cheen-Hau |
title |
Motion capture data recovery using skeleton constrained singular value thresholding |
title_short |
Motion capture data recovery using skeleton constrained singular value thresholding |
title_full |
Motion capture data recovery using skeleton constrained singular value thresholding |
title_fullStr |
Motion capture data recovery using skeleton constrained singular value thresholding |
title_full_unstemmed |
Motion capture data recovery using skeleton constrained singular value thresholding |
title_sort |
motion capture data recovery using skeleton constrained singular value thresholding |
publishDate |
2014 |
url |
https://hdl.handle.net/10356/103460 http://hdl.handle.net/10220/24494 |
_version_ |
1681040302620540928 |