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Content-Based Human Motion Retrieval with Automatic Transition

  • Yan Gao
  • Lizhuang Ma
  • Yiqiang Chen
  • Junfa Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)

Abstract

This paper presents a framework for efficient content-based motion retrieval. To bridge the gap between user’s vague perception and explicit motion scene description, we propose a Scene Description Language that can translate user’s input into a series of set operations between inverted lists. Our Scene Description Language has three-layer structures, each describing scenes at different levels of granularity. By introducing automatic transition strategy into our retrieval process, our system can search motions that do not exist in a motion database. This property makes our system have potentials to serve as motion synthesis purpose. Moreover, by using various kinds of qualitative features and adaptive segments of motion capture data stream, we obtain a robust clustering that is flexible and efficient for constructing motion graph. Some experimental examples are given to demonstrate the effectiveness and efficiency of proposed algorithms.

Keywords

Motion Segment Motion Type Automatic Transition Union Operation Inverted List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Park, S.I., Shin, H.J., Kim, T., Shin, S.Y.: On-line motion blending for real-time locomotion generation. Computer Animation and Virtual Worlds 15(3), 125–138 (2004)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Kovar, L., Gleicher, M., Pighin, F.: Motion graphs. ACM Transactions on Graphics 21(3), 473–482 (2002)CrossRefGoogle Scholar
  3. 3.
    Lee, J., Chai, J., Reitsma, P., Hodgins, J., Pollard, N.: Interactive control of avatars animated with human motion data. ACM Transactions on Graphics 21(3), 491–500 (2002)Google Scholar
  4. 4.
    CMU. Carnegie-Mellon Mocap Database (2003), http://mocap.cs.cmu.edu
  5. 5.
    Arikan, O., Forsyth, D.A.: Interactive Motion Generation from Examples. ACM Transactions on Graphics 21(3), 483–490 (2002)CrossRefMATHGoogle Scholar
  6. 6.
    Kovar, L., Gleicher, M.: Automated extraction and parameterization of motions in large data sets. ACM Transactions on Graphics 23(3), 559–568 (2004)CrossRefGoogle Scholar
  7. 7.
    Müller, M., Röder, T., Clausen, M.: Efficient content-based retrieval of motion capture data. ACM Transactions on Graphics 24(3), 677–685 (2005)CrossRefGoogle Scholar
  8. 8.
    Kim, T.H., Park, S.I., Shin, S.Y.: Rhythmic-motion synthesis based on motion-beat analysis. ACM Transactions on Graphics 22(3), 392–401 (2003)CrossRefGoogle Scholar
  9. 9.
    Kwon, T., Shin, S.Y.: Motion modeling for on-line locomotion synthesis. In: Proc. ACM SIGGRAPH/ Eurographics Symposium on Computer Animation, pp. 29–38 (2005)Google Scholar
  10. 10.
    Chiu, C., Chao, S., Wu, M., Yang, S., Lin, H.: Content-based retrieval for human motion data. Journal of Visual Communication and Image Representation, Special Issue on Multimedia Database Management Systems 15(3), 446–466 (2004)Google Scholar
  11. 11.
    Liu, F., Zhuang, Y., Wu, F., Pan, Y.: 3D motion retrieval with motion index tree. Computer Vision and Image Understanding 92, 265–284 (2003)CrossRefGoogle Scholar
  12. 12.
    Keogh, E.J., Palpanas, T., Zordan, V.B., Gunopulos, D., Cardle, M.: Indexing large human-motion databases. In: Proc. 30th VLDB Conf., pp. 780–791 (2004)Google Scholar
  13. 13.
    Müller, M., Röder, T., Clausen, M.: Efficient Indexing And Retrieval of Motion Capture Data Based on Adaptive Segmentation. In: Proceedings of the 4th Intl. Workshop on Content-Based Multimedia Indexing, Riga, Latvia (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yan Gao
    • 1
  • Lizhuang Ma
    • 1
  • Yiqiang Chen
    • 2
  • Junfa Liu
    • 2
  1. 1.Department of Computer Science & EngineeringShanghai Jiao Tong UniversityShanghaiP.R.C
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina

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