Automatic Classification and Retrieval of Motion Capture Data

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.
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.