Center for Human Modeling and Simulation

Document Type

Conference Paper

Date of this Version


Publication Source

Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games

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Last Page





MIG '15 was held November 16-18, 2015, in Paris.


Many interactive 3D games utilize motion capture for both character animation and user input. These applications require short, meaningful sequences of data. Manually producing these segments of motion capture data is a laborious, time-consuming process that is impractical for real-time applications. We present a method to automatically produce semantic segmentations of general motion capture data by examining the qualitative properties that are intrinsic to all motions, using Laban Movement Analysis (LMA). LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and lowlevel kinematic features, which often yield unsophisticated segmentations. Our method finds motion sequences which exhibit high output similarity from a collection of neural networks trained with temporal variance. We show that segmentations produced using LMA features are more similar to manual segmentations, both at the frame and the segment level, than several other automatic segmentation methods.

Copyright/Permission Statement

© Bouchard & Badler | ACM, 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 8th ACM SIGGRAPH Conference on Motion in Games,


human motion, motion capture, motion segmentation, Laban movement analysis



Date Posted: 13 January 2016