imprint:
Association for Computing Machinery (ACM), 2020
Published in:ACM Transactions on Graphics
Language:
English
DOI:
10.1145/3386569.3392381
ISSN:
0730-0301;
1557-7368
Origination:
Footnote:
Description:
<jats:p>
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
<jats:italic>experts</jats:italic>
, 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.
</jats:p>