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Efficient Content-Based Retrieval of Motion Capture Data

ACM SIGGRAPH 2005 paper by Meinard Müller, Tido Röder, and Michael Clausen


Abstract:

The reuse of human motion capture data to create new, realistic motions by applying morphing and blending techniques has become an important issue in computer animation. This requires the identification and extraction of logically related motions scattered within some data set. Such content-based retrieval of motion capture data, which is the topic of this paper, constitutes a difficult and time-consuming problem due to significant spatio-temporal variations between logically related motions. In our approach, we introduce various kinds of qualitative features describing geometric relations between specified body points of a pose and show how these features induce a time segmentation of motion capture data streams. By incorporating spatio-temporal invariance into the geometric features and adaptive segments, we are able to adopt efficient indexing methods allowing for flexible and efficient content-based retrieval and browsing in huge motion capture databases. Furthermore, we obtain an efficient preprocessing method substantially accelerating the cost-intensive classical dynamic time warping techniques for the time alignment of logically similar motion data streams. We present experimental results on a test data set of more than one million frames, corresponding to 180 minutes of motion. The linearity of our indexing algorithms guarantees the scalability of our results to much larger data sets.

Download a preprint version of our paper here: (PDF, 2.6 MB.)

Download the accompanying video here: [English version]; [German version] (AVI files in DivX5 format, 31 MB.)

(c) ACM, 2005. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version has been published in ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH 2005).