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Jernej Barbic, Alla Safonova, Jia-Yu Pan, Christos Faloutsos, Jessica K. Hodgins, and Nancy S. Pollard. Segmenting Motion
Capture Data into Distinct Behaviors. In Proceedings of Graphics Interface (GI 2004), 2004.
London, Ontario,
Canada, May 17-19, 2004
Much of the motion capture data used in animations, commercials, and video games is carefully segmented into distinct motions
either at thetime of capture or by hand after the capture session. As we movetoward collecting more and longer motion sequences,
however, automaticsegmentation techniques will become important for processing theresults in a reasonable time frame.
We
have found that straightforward, easy to implement segmentationtechniques can be very effective for segmenting motion sequences
intodistinct behaviors. In this paper, we present three approaches forautomatic segmentation. The first two approaches are
online, meaningthat the algorithm traverses the motion from beginning to end,creating the segmentation as it proceeds. The
first assigns a cutwhen the intrinsic dimensionality of a local model of the motionsuddenly increases. The second places
a cut when the distribution ofposes is observed to change. The third approach is a batch processand segments the sequence
where consecutive frames belong to differentelements of a Gaussian mixture model. We assess these three methods onfourteen
motion sequences and compare the performance of the automaticmethods to that of transitions selected manually.
@InProceedings{GI04SegMocap,
author = {Jernej Barbic and Alla Safonova and Jia-Yu Pan and Christos Faloutsos and Jessica K. Hodgins and Nancy S. Pollard},
title = {Segmenting Motion Capture Data into Distinct Behaviors},
booktitle = {Proceedings of Graphics Interface (GI 2004)},
year = 2004,
wwwnote = {London, Ontario, Canada, May 17-19, 2004},
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. <br>
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.},
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {Stream Data Mining},
}
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