用强化学习优化扩散模型，生成更符合物理规律的人体动作。
ReinDiffuse: Crafting Physically Plausible Motions with Reinforced Diffusion Model
- 将运动扩散模型改为输出动作分布，适配强化学习框架。
- 在HumanML3D和KIT-ML数据集上，物理合理性显著提升。
- 适合需要真实感人体动作生成的研究与应用。
从文本描述生成人体动作是一项挑战性任务。现有方法或难以保证物理真实性，或受限于物理模拟的复杂性。本文提出ReinDiffuse，结合强化学习与运动扩散模型，生成与文本描述一致且具有物理合理性的动作。该方法将运动扩散模型改造为输出动作参数化分布，使其兼容强化学习范式。通过最大化物理合理性奖励的目标，优化动作生成以增强物理保真度。在两个主流数据集HumanML3D和KIT-ML上，本方法优于现有最先进模型，在物理合理性与动作质量上均取得显著提升。
原文摘要 · Abstract (English)
Generating human motion from textual descriptions is a challenging task. Existing methods either struggle with physical credibility or are limited by the complexities of physics simulations. In this paper, we present \emph{ReinDiffuse} that combines reinforcement learning with motion diffusion model to generate physically credible human motions that align with textual descriptions. Our method adapts Motion Diffusion Model to output a parameterized distribution of actions, making them compatible with reinforcement learning paradigms. We employ reinforcement learning with the objective of maximizing physically plausible rewards to optimize motion generation for physical fidelity. Our approach outperforms existing state-of-the-art models on two major datasets, HumanML3D and KIT-ML, achieving significant improvements in physical plausibility and motion quality. Project: https://reindiffuse.github.io/
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