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

NaP-Control uses reinforcement learning to steer a diffusion policy’s latent noise, aiming for whole-body character control that is both fast and robust. The approach skips iterative test-time guidance, which can cut inference cost while keeping motion natural. For teams building physics-based animation, that could mean more responsive control without sacrificing fidelity.

First reported 4 months, 1 week ago • character-control diffusion-models reinforcement-learning animation
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NaP-Control: Navigating Diffusion Prior for Versatile and Fast Character Control

4 months, 1 week ago Read source
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