ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters
Abstract
In this paper, we introduce ControlVAE, a novel model-based framework for learning generative motion control policies based on variational autoencoders (VAE). Our framework can learn a rich and flexible latent representation of skills and a skill-conditioned generative control policy from a diverse set of unorganized motion sequences, which enables the generation of realistic human behaviors by sampling in the latent space and allows high-level control policies to reuse the learned skills to accomplish a variety of downstream tasks. In the training of ControlVAE, we employ a learnable world model to realize direct supervision of the latent space and the control policy. This world model effectively captures the unknown dynamics of the simulation system, enabling efficient model-based learning of high-level downstream tasks. We also learn a state-conditional prior distribution in the VAE-based generative control policy, which generates a skill embedding that outperforms the non-conditional priors in downstream tasks. We demonstrate the effectiveness of ControlVAE using a diverse set of tasks, which allows realistic and interactive control of the simulated characters.
Cite
@article{arxiv.2210.06063,
title = {ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters},
author = {Heyuan Yao and Zhenhua Song and Baoquan Chen and Libin Liu},
journal= {arXiv preprint arXiv:2210.06063},
year = {2022}
}
Comments
SIGGRAPH Asia 2022 (Journal Track);