Scalable and compact representation for motion capture data using tensor decomposition

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Article number6708414
Pages (from-to)255-259
Number of pages5
JournalIEEE Signal Processing Letters
Issue number3
Publication statusPublished - Mar 2014
Externally publishedYes


  • Compression
  • decomposition
  • motion capture
  • tensor

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics


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