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 talk, constitutes a difficult 
and time-consuming problem due to significant spatio-temporal 
variations between logically related motions. Recent 
approaches to motion retrieval apply techniques such as 
dynamic time warping which, however, due to their quadratic 
space and time complexity, are not applicable to large data 
sets. In our approach, we introduce various kinds of boolean
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.
Finally, a new method for automatic motion classification
is presented. Using relational motion features, we introduce 
the concept of motion templates, by which the essence of an 
entire class of logically related motions can be captured.