arXiv:2605.20209v1 Announce Type: cross Abstract: 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 ta
The rapid advancement of diffusion models in AI is pushing the boundaries of what's possible in animation and robotics control, leading to innovative applications like NaP-Control.
This development allows for more versatile, precise, and faster character control in simulations, which is critical for the realism and efficiency of physics-based animation and robotics.
The reliance on slow, often brittle gradient-based guidance for diffusion models in character control is reduced, replaced by a faster, more robust reinforcement learning approach.
- · AI researchers
- · Robotics developers
- · Animation studios
- · Game development
- · Traditional animation techniques
- · Less efficient control methods
NaP-Control significantly improves the efficiency and expressiveness of virtual character and robot movements.
This improved control precision could accelerate the development and deployment of more agile and capable humanoid robots and AI agents in complex environments.
Advanced character control might lead to more realistic digital humans across metaverse applications, virtual assistants, and entertainment experiences, blurring lines between digital and physical.
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Read at arXiv cs.LG