Computer Science > Graphics
[Submitted on 3 Jun 2024 (v1), last revised 4 Dec 2024 (this version, v2)]
Title:PDP: Physics-Based Character Animation via Diffusion Policy
View PDF HTML (experimental)Abstract:Generating diverse and realistic human motion that can physically interact with an environment remains a challenging research area in character animation. Meanwhile, diffusion-based methods, as proposed by the robotics community, have demonstrated the ability to capture highly diverse and multi-modal skills. However, naively training a diffusion policy often results in unstable motions for high-frequency, under-actuated control tasks like bipedal locomotion due to rapidly accumulating compounding errors, pushing the agent away from optimal training trajectories. The key idea lies in using RL policies not just for providing optimal trajectories but for providing corrective actions in sub-optimal states, giving the policy a chance to correct for errors caused by environmental stimulus, model errors, or numerical errors in simulation. Our method, Physics-Based Character Animation via Diffusion Policy (PDP), combines reinforcement learning (RL) and behavior cloning (BC) to create a robust diffusion policy for physics-based character animation. We demonstrate PDP on perturbation recovery, universal motion tracking, and physics-based text-to-motion synthesis.
Submission history
From: Takara Truong [view email][v1] Mon, 3 Jun 2024 03:31:37 UTC (5,328 KB)
[v2] Wed, 4 Dec 2024 21:46:41 UTC (5,921 KB)
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