  In this paper, we present a simulation and control framework for generating
biomechanically plausible motion for muscle-actuated characters. We incorporate
a fatigue dynamics model, the 3CC-r model, into the widely-adopted Hill-type
muscle model to simulate the development and recovery of fatigue in muscles,
which creates a natural evolution of motion style caused by the accumulation of
fatigue from prolonged activities. To address the challenging problem of
controlling a musculoskeletal system with high degrees of freedom, we propose a
novel muscle-space control strategy based on PD control. Our simulation and
control framework facilitates the training of a generative model for
muscle-based motion control, which we refer to as MuscleVAE. By leveraging the
variational autoencoders (VAEs), MuscleVAE is capable of learning a rich and
flexible latent representation of skills from a large unstructured motion
dataset, encoding not only motion features but also muscle control and fatigue
properties. We demonstrate that the MuscleVAE model can be efficiently trained
using a model-based approach, resulting in the production of high-fidelity
motions and enabling a variety of downstream tasks.
