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

Kensuke Onuma, Christos Faloutsos, Jessica K. Hodgins

Short Papers Proceedings of EUROGRAPHICS (2008)

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.

Kensuke Onuma, Christos Faloutsos, Jessica K. Hodgins (2008). FMDistance: A fast and Effective Distance Function for Motion Capture Data.

*Short Papers Proceedings of EUROGRAPHICS.*

@inproceedings{Onuma:2008:fmdistance,

author = {Kensuke Onuma and Christos Faloutsos and Jessica K. Hodgins},

title = {FMDistance: A fast and Effective Distance Function for Motion

Capture Data},

booktitle = {Short Papers Proceedings of EUROGRAPHICS},

year = {2008},

links = {http://graphics.cs.cmu.edu/papers/OnumaEG2008.pdf},

}