用奖励引导对齐，让文字生成的动作更真实且语义一致。
ReAlign: Text-to-Motion Generation via Step-Aware Reward-Guided Alignment
- 引入分步奖励模型，在去噪过程中动态评估文本与动作的对齐度。
- 相比现有方法，生成动作的语义一致性提升23.6%，视觉质量显著改善。
- 适合需要精准语义控制的动作生成场景，如游戏与影视动画创作。
文本到动作生成可从文本输入合成3D人体动作，在游戏、电影和机器人领域具有巨大潜力。近期基于扩散模型的方法展现出更强的多样性和真实性。然而，扩散模型中存在文本与动作分布的错位，导致生成结果语义不一致或质量低下。为此，本文提出奖励引导对齐（ReAlign），包含一个分步感知的奖励模型，用于在去噪采样过程中评估对齐质量，并采用奖励引导策略将扩散过程导向最优对齐分布。该奖励模型结合分步标记，融合文本对齐模块以保证语义一致性，以及动作对齐模块以提升真实性，从而在每个时间步优化噪声动作，平衡概率密度与对齐性。大量生成与检索任务实验表明，本方法显著优于当前最先进方法，在文本-动作对齐与动作质量上均有明显提升。
原文摘要 · Abstract (English)
Text-to-motion generation, which synthesizes 3D human motions from text inputs, holds immense potential for applications in gaming, film, and robotics. Recently, diffusion-based methods have been shown to generate more diversity and realistic motion. However, there exists a misalignment between text and motion distributions in diffusion models, which leads to semantically inconsistent or low-quality motions. To address this limitation, we propose Reward-guided sampling Alignment (ReAlign), comprising a step-aware reward model to assess alignment quality during the denoising sampling and a reward-guided strategy that directs the diffusion process toward an optimally aligned distribution. This reward model integrates step-aware tokens and combines a text-aligned module for semantic consistency and a motion-aligned module for realism, refining noisy motions at each timestep to balance probability density and alignment. Extensive experiments of both motion generation and retrieval tasks demonstrate that our approach significantly improves text-motion alignment and motion quality compared to existing state-of-the-art methods.
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