FMDistance: A Fast and Effective Distance Function for Motion Capture Data
Conference Paper, Proceedings of Eurographics '08, pp. 83 - 86, April, 2008
Abstract
Given several motion capture sequences, of similar (but not identical) length, what is a good distance function? We want to find similar sequences, to spot outliers, to create clusters, and to visualize the (large) set of motion capture sequences at our disposal. We propose a set of new features for motion capture sequences. We experiment with numerous variations (112 feature-sets in total, using variations of weights, logarithms, dimensionality reduction), and we show that the appropriate combination leads to near-perfect classification on a database of 226 actions with twelve different categories, and it enables visualization of the whole database as well as outlier detection.
BibTeX
@conference{Onuma-2008-122013,author = {Kensuke Onuma and Christos Faloutsos and Jessica K. Hodgins},
title = {FMDistance: A Fast and Effective Distance Function for Motion Capture Data},
booktitle = {Proceedings of Eurographics '08},
year = {2008},
month = {April},
pages = {83 - 86},
}
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.