NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control

Achieving precise, versatile whole-body character control inphysics-based animation remains challenging. Recent diffusion-based poli-cies generate rich and expressive motions but typically rely on gradient-based test-time guidance to satisfy task objectives, which is slow andcan reduce robustness. We introduce NaP-Control (Navigating Diffu-sion Prior for Versatile and Fast Character Control), abbreviated asNaP. Our method uses reinforcement learning to manipulate the latentnoise of a task-agnostic diffusion policy prior, steering it toward task-specific behaviors for fast, robust control with high motion fidelity. Incontrast to methods that rely solely on offline training, NaP interactswith the environment during training to correct motions and optimizetask rewards, improving success rates and enabling adaptation to chal-lenging scenarios. By directly predicting task-optimized diffusion noise,NaP eliminates iterative guidance during denoising and enables efficientinference. Experiments show that NaP attains higher success rates andfaster inference while preserving natural motion across diverse tasks.