Computer Science Department
School of Computer Science, Carnegie Mellon University


FMDistance: A Fast and Effective Distance
Function for Motion Capture Data

Kensuke Onuma*, Christos Faloutsos, Jessica K. Hodgins

April 2008


Keywords: Data mining, motion capture, distance function, classification, human animation

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.

18 pages

*Sony Corporation, Tokyo, Japan

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