Movement Classes from Human Motion Data

  • Kang Hoon Lee
  • Jong Pil Park
  • Jehee Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6758)


We present a new method for identifying a set of movement types from unlabelled human motion data. One typical approach first segments input motion into a series of intervals, and then clusters those into a set of groups. Unfortunately, the dependency between segmentation and clustering causes trouble in alternate tuning of parameters. Instead, we unify those two tasks in a single optimization framework that searches for the optimal segmentation maximizing the quality of clustering. The genetic algorithm is employed to address this combinatorial problem with our own genetic representation and fitness function. As the primary benefit, the user is able to obtain a repertoir of major movements just by selecting the number of classses to be identified. We demonstrate the usefulness of our approach by providing visual descriptions of motion data, and an intuitive animation authoring interface based on movement collections.


computer animation human motion data movement classfication 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kang Hoon Lee
    • 1
  • Jong Pil Park
    • 2
  • Jehee Lee
    • 2
  1. 1.Kwangwoon UniversitySeoulKorea
  2. 2.Seoul National UniversitySeoulKorea

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