NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control
For physics-based character animation, NaP-Control provides a versatile and fast control method that outperforms gradient-based guidance approaches in success rate and inference speed.
NaP-Control uses reinforcement learning to manipulate the latent noise of a diffusion policy prior for fast, robust character control, achieving higher success rates and faster inference while preserving natural motion across diverse tasks.
Achieving precise, versatile whole-body character control in physics-based animation remains challenging. Recent diffusion-based policies generate rich and expressive motions but typically rely on gradient-based test-time guidance to satisfy task objectives, which is slow and can reduce robustness. We introduce NaP-Control (Navigating Diffusion Prior for Versatile and Fast Character Control), abbreviated as NaP. Our method uses reinforcement learning to manipulate the latent noise of a task-agnostic diffusion policy prior, steering it toward task-specific behaviors for fast, robust control with high motion fidelity. In contrast to methods that rely solely on offline training, NaP interacts with the environment during training to correct motions and optimize task rewards, improving success rates and enabling adaptation to challenging scenarios. By directly predicting task-optimized diffusion noise, NaP eliminates iterative guidance during denoising and enables efficient inference. Experiments show that NaP attains higher success rates and faster inference while preserving natural motion across diverse tasks.