An Efficient Keyframe Extraction from Motion Capture Data

  • Jun Xiao
  • Yueting Zhuang
  • Tao Yang
  • Fei Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4035)


This paper proposes a keyframe extraction method based on a novel layered curve simplification algorithm for motion capture data. Bone angles are employed as motion features and keyframe candidates can be selected based on them. After that, the layered curve simplification algorithm will be used to refine those candidates and the keyframe collection can be gained. To meet different requirements for compression and level of detail of motion abstraction, adaptive extraction parameters are also applied. The experiments demonstrate that our method can not only compress and summarize the motion capture data efficiently, but also keep the consistency of keyframe collection between similar human motion sequences, which is of great benefit to further motion data retrieval or editing.


Compression Ratio Human Motion Motion Sequence Motion Capture Data Original Motion 
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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  1. 1.
    Terra, S.C.L., Metoyer, R.A.: Performance Timing for Keyframe Animation. In: Proceedings of Eurographics/ACM SIGGRAPH Symposium on Computer Animation, pp. 253–258 (2004)Google Scholar
  2. 2.
    Whitaker, H., Halas, J.: Timing for Animation. Focal Press, Burlington (1981)Google Scholar
  3. 3.
    Parent, R.: Computer Animation: Algorithms and Techniques. Morgan Kaufmann, San Francisco (2001)Google Scholar
  4. 4.
    Liu, F., et al.: 3d Motion Retrieval with Motion Index Tree[J]. Computer Vision and Image Understanding 92(2-3), 265–284 (2003)CrossRefGoogle Scholar
  5. 5.
    Junxing, S., Shouqian, S., Yunhe, P.: Key-Frame Extraction from Motion Capture Data. Journal of Computer-aided Design & Computer Graphics 16(5), 719–723 (2004)Google Scholar
  6. 6.
    Lim, I.S., Thalmann, D.: Key-Posture Extraction Out of Human Motion Data by Curve Simplification[C]. In: Proc. EMBC2001, 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1167–1169 (2001)Google Scholar
  7. 7.
    Pickering, M.J., Ruger, S.: Evaluation of key frame-based retrieval techniques for video. Comput. Vis. Image Understand 92(2), 217–235 (2003)CrossRefGoogle Scholar
  8. 8.
    Zhuang, Y., Rui, Y., Huang, T.S.: Adaptive Key Frame Extraction Using Unsupervised Clustering[A]. In: IEEE ICIP 1998, Chicago, USA (October 1998)Google Scholar
  9. 9.
    Zhuang, Y., Pan, Y., Wu, F.: Web-based Multimedia Information Analysis and Retrieval. Tsinghua University Press (2002)Google Scholar
  10. 10.
    Barbic, J., Safonova, A., Pan, J.-Y., Faloutsos, C., Hodgins, J.K., Pollard, N.S.: Segmenting Motion Capture Data into Distinct Behaviors. In: Proceedings of Graphics Interface, pp. 185–194 (2004)Google Scholar
  11. 11.
    Brand, M.E., Kettnaker, V.: Discovery and segmentation of activities in video. IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI) 22(8), 844–851 (2000)CrossRefGoogle Scholar
  12. 12.
    Park, M.J., Shin, S.Y.: Example-based motion cloning. Comput. Animat. Virtual Worlds 15(3-4), 245–257 (2004)CrossRefGoogle Scholar
  13. 13.
    Huang, K.-S., Chang, C.-F., Hsu, Y.-Y., Yang, S.-N.: Key Probe: a technique for animation keyframe extraction. The Visual Computer 21(8-10), 532–541 (2005)CrossRefGoogle Scholar
  14. 14.
    Lee, J., Chai, J., Reitsma, P.S.A., Hodgins, J.K., Pollard, N.S.: Interactive Control of Avatars Animated with Human Motion Data[A]. In: Proceedings SIGGRAPH 2002[C], San Antonio, Texas, pp. 491–500 (2002)Google Scholar
  15. 15.
    Chui, C.Y., Chao, S.P., Wu, M.Y., Yang, S.N., Lin, H.C.: Content-based Retrieval for Human Motion Data[J]. Journal of Visual Communication and Image Representation 16(3), 446–466 (2004)CrossRefGoogle Scholar
  16. 16.
    Mueller, M., Roeder, T., Clausen, M.: Efficient Content-Based Retrieval of Motion Capture Data[A]. In: Proceedings of ACM SIGGRAPH 2005[C], vol. 24(3), pp. 677–685 (2005)Google Scholar
  17. 17.
    Rosin, P.L.: Techniques for Assessing Polygonal Approximations of Curve[J]. IEEE Transactions on Pattern Recognition and Machine Intelligence 19(6), 659–666 (1997)CrossRefGoogle Scholar
  18. 18.
    Fritsch, F.N., Carlson, R.E.: Monotone Piecewise Cubic Interpolation. SIAM J. Numerical Analysis 17, 238–246 (1980)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Xiao
    • 1
  • Yueting Zhuang
    • 1
  • Tao Yang
    • 1
  • Fei Wu
    • 1
  1. 1.Institute of Artificial IntelligenceZhejiang UniversityHangzhouP.R. China

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