Musculoskeletal Motion Control and Generation Based on Muscle Synergies

ABSTRACT This paper presents a unified two‐stage framework for physics‐based musculoskeletal motion control and generation. To tackle the challenges posed by high dimensionality and redundancy, we first employ an autoencoder to learn a low‐dimensional muscle synergy space from activation data. Policies trained in this space make the character faithfully reproduce motions while generating physiologically plausible muscle activations. We then leverage these expert trajectories to train a Conditional VAE, encoding skills into a continuous latent space for task‐agnostic motion synthesis and downstream control. Experiments show our method achieves high motion imitation accuracy and generation diversity, ensures control stability, and maintains physiological realism, offering an effective solution for generalizing control of complex musculoskeletal characters.