A group of novel approaches and a toolkit for motion capture data reusing

  • Authors:
  • Jun Xiao;Yueting Zhuang;Fei Wu;Tongqiang Guo;Zhang Liang

  • Affiliations:
  • Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China 310027;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China 310027;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China 310027;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China 310027;Institute of Artificial Intelligence, Zhejiang University, Hangzhou, China 310027

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2010

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Abstract

Now more and more motion capture (MoCap) systems are used to acquire realistic and highly detailed motion data which are widely used for producing animations of human-like characters in a variety of applications, such as simulations, video games and animation films. And recently large MoCap databases are available. As a kind of emerging multimedia data, 3D human motion has its own specific data form and standard format. But to the best of our knowledge, only a few approaches have been explored for 3D MoCap data feature representation and reusing. This paper proposes a group of novel approaches for posture feature representation, motion sequence segmentation, key-frame extraction and content-based motion retrieval, which are all very important for MoCap data reusing and benefit to the efficient animation production. To validate these approaches, we set up a MoCap database and implemented a prototype toolkit. The experiments show that the proposed algorithms could achieve the approvable results.