Scalable and compact representation for motion capture data using tensor decomposition

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

16 Scopus Citations
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Author(s)

  • Junhui Hou
  • Lap-Pui Chau
  • Nadia Magnenat-Thalmann
  • Ying He

Detail(s)

Original languageEnglish
Article number6708414
Pages (from-to)255-259
Journal / PublicationIEEE Signal Processing Letters
Volume21
Issue number3
Publication statusPublished - Mar 2014
Externally publishedYes

Abstract

Motion capture (mocap) technology is widely used in movie and game industries. Compact representation of the mocap data is critical to efficient storage and transmission. In this letter, we propose a novel tensor decomposition based scheme for compact and progressive representation of the mocap data. Our method segments and stacks the mocap sequence locally, and generates a 3rd-order tensor, which has strong correlation within and across slices of the tensor. Then, our method iteratively applies tensor decomposition in a multi-layer structure to explore the correlation characteristic. Experimental results demonstrate that the proposed scheme significantly outperforms existing algorithms in terms of scalability and storage requirement. © 1994-2012 IEEE.

Research Area(s)

  • Compression, decomposition, motion capture, tensor

Citation Format(s)

Scalable and compact representation for motion capture data using tensor decomposition. / Hou, Junhui; Chau, Lap-Pui; Magnenat-Thalmann, Nadia; He, Ying.

In: IEEE Signal Processing Letters, Vol. 21, No. 3, 6708414, 03.2014, p. 255-259.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review