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A Scalable Approach to Control Diverse Behaviors for Physically Simulated Characters
Cited 60 time in
Web of Science
Cited 66 time in Scopus
- Authors
- Issue Date
- 2020-07
- Citation
- ACM Transactions on Graphics, Vol.39 No.4, p. 3392381
- Abstract
- Human characters with a broad range of natural looking and physically realistic behaviors will enable the construction of compelling interactive experiences. In this paper, we develop a technique for learning controllers for a large set of heterogeneous behaviors. By dividing a reference library of motion into clusters of like motions, we are able to construct experts, learned controllers that can reproduce a simulated version of the motions in that cluster. These experts are then combined via a second learning phase, into a general controller with the capability to reproduce any motion in the reference library. We demonstrate the power of this approach by learning the motions produced by a motion graph constructed from eight hours of motion capture data and containing a diverse set of behaviors such as dancing (ballroom and breakdancing), Karate moves, gesturing, walking, and running.
- ISSN
- 0730-0301
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