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.

翻译：在基于物理的动画中实现精确、通用的全身角色控制仍然具有挑战性。最近的基于扩散的策略可生成丰富且表现力强的运动，但通常依赖基于梯度的测试时引导来满足任务目标，这速度较慢且可能降低鲁棒性。我们提出NaP-Control（导航扩散先验以实现多用途且快速的角色控制），缩写为NaP。该方法利用强化学习操控任务无关的扩散策略先验的潜在噪声，将其导向任务特定行为，以实现快速、鲁棒的控制并保持高运动保真度。与仅依赖离线训练的方法不同，NaP在训练过程中与环境交互以修正运动并优化任务奖励，从而提高成功率并适应具有挑战性的场景。通过直接预测任务优化的扩散噪声，NaP消除了去噪过程中的迭代引导，并实现了高效推理。实验表明，NaP在保持多样化任务中自然运动的同时，实现了更高的成功率和更快的推理速度。