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Abstract

In the last years, various algorithms have been proposed for automatic
classification and retrieval of motion capture data. Here, one main
difficulty is due to the fact that similar types of motions may exhibit
significant spatial as well as temporal variations. To cope with such
variations, previous algorithms often rely on warping and alignment
techniques that are computationally time and cost intensive.
In this paper, we present a novel keyframe-based algorithm that
significantly speeds up the retrieval process and drastically
reduces memory requirements.
In contrast to previous index-based strategies, our recursive algorithm
can cope with temporal variations. In particular, the degree of
admissible deformation tolerance between the queried keyframes
can be controlled by an explicit stiffness parameter.
While our algorithm works for general multimedia data,
we concentrate on demonstrating the practicability of
our concept by means of the motion retrieval scenario.
Our experiments show that one can typically cut down the
search space from several hours to a couple of minutes of
motion capture data within a fraction of a second.