Active Learning for Real-time Motion Controllers
This paper describes an approach to building real-time highly-controllable characters. A kinematic character controller is built on-the-fly during a capture session, and updated after each new motion clip is acquired. Active learning is used to identify which motion sequence the user should perform next, in order to improve the quality and responsiveness of the controller. Because motion clips are selected adaptively, we avoid the difficulty of manually determining which ones to capture, and can build complex controllers from scratch while significantly reducing the number of necessary motion samples.
Project Members
Seth Cooper
Aaron Hertzmann
Zoran Popović
Active Learning for Real-time Motion Controllers
Cooper, S. Hertzmann, A. Popović, Z.
ACM Transactions on Graphics 26(3) (SIGGRAPH 2007)
[Paper (2.4 Mb)]
SIGGRAPH video (5 mins) - (Requires Quicktime to view.)
[Movie (117 Mb)]
University of Washington Animation Research Labs
National Science Foundation
Alfred P. Sloan Fellowship
Natural Sciences and Engineering Research Council of Canada
Canadian Foundation for Innovation
Electronic Arts
Microsoft Research