PhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization
Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion data. Building on this progress, recent methods attempt to transfer such models for character animation and real robot control by applying a Whole-Body Controller (WBC) that converts diffusion-generated motions into executable trajectories. While WBC trajectories become compliant with physics, they may expose substantial deviations from original motion. To address this issue, we here propose PhysMoDPO, a Direct Preference Optimization framework. Unlike prior work that relies on hand-crafted physics-aware heuristics such as foot-sliding penalties, we i...
| Authors | Yangsong Zhang, Anujith Muraleedharan, Rikhat Akizhanov et al. |
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| Primary category | cs.LG |
| Published | 13 March 2026 |
| Last updated at source | 8 October 2026 |
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