In this paper we propose a unified approach to efficient indexing and content-based retrieval of human motion capture data as used in data-driven computer animation or computer vision. Opposed to other data formats such as music or video, the kinematic chain (a kind of human skeleton) as underlying model of motion capture data allows to introduce qualitative boolean features describing geometric re lations of specified points of the body. In combination with such geometric features, we introduce the concept of adaptive temporal segmentation of motion data streams, which accounts for the spatio-temporal invariance needed to iden tify logically related motions. This allows us to adopt efficient indexing and fault-tolerant retrieval methods such as fuzzy search. Here, the crucial point is that our adaptive se gmentation not only adjusts to the granularity of the feature function but also to the fuzziness of the query. We present experimental results on a test data set of more than one million frames corresponding to 180 minutes of motion capture data. The linearity of our indexing algorithms guarantees the scalability of our results to much larger data sets.
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