ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters

Transactions on Graphics (Proc. ACM SIGGRAPH Asia 2022)

Heyuan Yao (1,2)   Zhenhua Song (1,2)    Baoquan Chen (2,3)    Libin Liu† (2,3)

(1) School of Computer Science, Peking University
(2) Key Laboratory of Machine Perception(MOE), Peking University
(3) School of Intelligence Science and Technology, Peking University



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.

Paper: [PDF]       Preprint: [arXiv]       Video: [Youtube | Bilibili]       Code: [Github (Coming Soon)]



Prediction of world model:

Random sampling in the latent space:

Speed/Style and direction control with user input:

Resistance to external perturbations:

For more details, please refer to our paper.