Recent progress in video generation has led to impressive visual quality, yet current models still struggle to produce results that align with real-world physical principles. To this end, we propose an iterative self-refinement framework that leverages large language models and vision-language models to provide physics-aware guidance for video generation. Specifically, we introduce a multimodal chain-of-thought (MM-CoT) process that refines prompts based on feedback from physical inconsistencies, progressively enhancing generation quality. This method is training-free and plug-and-play, making it readily applicable to a wide range of video generation models. Experiments on the PhyIQ benchmark show that our method improves the Physics-IQ score from 56.31 to 62.38. We hope this work serves as a preliminary exploration of physics-consistent video generation and may offer insights for future research.

翻译：近期视频生成技术已取得显著的视觉质量提升，但现有模型在生成符合真实世界物理规律的结果方面仍存在不足。为此，我们提出一种迭代自优化框架，利用大语言模型与视觉语言模型为视频生成提供物理感知引导。具体而言，我们引入多模态思维链过程，该过程基于物理不一致性反馈对生成提示进行迭代优化，从而逐步提升生成质量。该方法无需额外训练且具备即插即用特性，可广泛应用于各类视频生成模型。在PhyIQ基准测试上的实验表明，我们的方法将物理智商分数从56.31提升至62.38。本研究旨在为物理一致性视频生成提供初步探索，并为未来研究提供参考方向。