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Creating long gait animation sequences through Reinforcement Learning
Marco Alamia, N. Alberto Borghese
144 - 151
10.3233/978-1-60750-692-8-144
Frontiers in Artificial Intelligence and Applications
Volume 226: Neural Nets WIRN10
Abstract

In this paper we present how, using a careful definition of a state function, long animation sequences can be created joining clips from a database. Each next clip is chosen in real-time by a controller optimizing a cost function on the state function; this allows the user interact in real-time with the digital character. We analyze here two possible cost functions, one that is based on the evaluation of the compatibility of the next clip and one based on reinforcement learning in which the global policy of the controller is evaluated.

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