统一规划与控制的扩散模型，让角色动作更自然且适应多种指令。
UniPhys: Unified Planner and Controller with Diffusion for Flexible Physics-Based Character Control
- 用扩散模型统一建模运动规划与物理控制，减少领域差异。
- 在长序列中保持动作自然，支持文本、轨迹等多模态输入。
- 无需微调即可泛化到未见任务，适合复杂场景下的角色生成。
生成自然且符合物理规律的角色动作仍具挑战性，尤其在长时序控制和多样引导信号下。现有方法虽将高层扩散运动规划与低层物理控制器结合，但存在领域差距导致动作质量下降，且需针对任务微调。为此，本文提出UniPhys，一种基于扩散的行为克隆框架，将运动规划与控制统一于单一模型。UniPhys可灵活生成受文本、轨迹、目标等多模态输入引导的表达性动作。为缓解长序列中的累积预测误差，采用扩散强制训练策略，学习对噪声运动历史去噪并处理物理仿真引入的偏差。该设计使UniPhys能鲁棒生成符合物理规律的长时序动作。通过引导采样，UniPhys在无需任务微调的情况下泛化至多种控制信号（包括未见信号）。实验表明，UniPhys在动作自然度、泛化能力与鲁棒性方面均优于现有方法。
原文摘要 · Abstract (English)
Generating natural and physically plausible character motion remains challenging, particularly for long-horizon control with diverse guidance signals. While prior work combines high-level diffusion-based motion planners with low-level physics controllers, these systems suffer from domain gaps that degrade motion quality and require task-specific fine-tuning. To tackle this problem, we introduce UniPhys, a diffusion-based behavior cloning framework that unifies motion planning and control into a single model. UniPhys enables flexible, expressive character motion conditioned on multi-modal inputs such as text, trajectories, and goals. To address accumulated prediction errors over long sequences, UniPhys is trained with the Diffusion Forcing paradigm, learning to denoise noisy motion histories and handle discrepancies introduced by the physics simulator. This design allows UniPhys to robustly generate physically plausible, long-horizon motions. Through guided sampling, UniPhys generalizes to a wide range of control signals, including unseen ones, without requiring task-specific fine-tuning. Experiments show that UniPhys outperforms prior methods in motion naturalness, generalization, and robustness across diverse control tasks.
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