Proceedings: GI 2004

Segmenting motion capture data into distinct behaviors

Jernej Barbič , Alla Safonova , Jia-Yu Pan , Christos Faloutsos , Jessica Hodgins , Nancy Pollard

Proceedings of Graphics Interface 2004: London, Ontario, Canada, 17 - 19 May 2004, 185-194

DOI 10.20380/GI2004.23

  • Bibtex

    @inproceedings{Barbi{\v c}:2004:10.20380/GI2004.23,
    author = {Barbi{\v c}, Jernej and Safonova, Alla and Pan, Jia-Yu and Faloutsos, Christos and Hodgins, Jessica and Pollard, Nancy},
    title = {Segmenting motion capture data into distinct behaviors},
    booktitle = {Proceedings of Graphics Interface 2004},
    series = {GI 2004},
    year = {2004},
    issn = {0-89791-213-6},
    isbn = {1-56881-227-2},
    location = {London, Ontario, Canada},
    pages = {185--194},
    numpages = {10},
    doi = {10.20380/GI2004.23},
    publisher = {Canadian Human-Computer Communications Society},
    address = {School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada},
    keywords = {Sampling},
    }

Abstract

Much of the motion capture data used in animations, commercials, and video games is carefully segmented into distinct motions either at the time of capture or by hand after the capture session. As we move toward collecting more and longer motion sequences, however, automatic segmentation techniques will become important for processing the results in a reasonable time frame.We have found that straightforward, easy to implement segmentation techniques can be very effective for segmenting motion sequences into distinct behaviors. In this paper, we present three approaches for automatic segmentation. The first two approaches are online, meaning that the algorithm traverses the motion from beginning to end, creating the segmentation as it proceeds. The first assigns a cut when the intrinsic dimensionality of a local model of the motion suddenly increases. The second places a cut when the distribution of poses is observed to change. The third approach is a batch process and segments the sequence where consecutive frames belong to different elements of a Gaussian mixture model. We assess these three methods on fourteen motion sequences and compare the performance of the automatic methods to that of transitions selected manually.