无需人工标注，从真实视频中自动学习物理规律生成更逼真的视频。
RDPO: Real Data Preference Optimization for Physics Consistency Video Generation
- 从真实视频中反向采样构建物理合理性对比对
- 多阶段迭代训练使生成视频物理一致性显著提升
- 适合追求真实物理行为的视频生成研究者
视频生成技术在视觉质量上已取得显著进展，但真实世界物理规律的准确再现仍具挑战。基于偏好的模型后训练虽可提升物理一致性，但依赖昂贵的人工标注数据或尚不成熟的奖励模型。为此，我们提出无标注框架RDPO，直接从真实视频中提取物理先验。具体而言，该方法利用预训练生成器反向采样真实视频序列，自动生成在物理正确性上具有统计可区分性的偏好对。通过多阶段迭代训练流程，引导生成器逐步遵循物理规律。得益于从真实视频中挖掘的动态信息，RDPO显著提升了生成视频的动作连贯性与物理真实性。在多个基准测试和人工评估中均验证了其在多维度上的改进效果。论文源码与演示见：https://wwenxu.github.io/RDPO/
原文摘要 · Abstract (English)
Video generation techniques have achieved remarkable advancements in visual quality, yet faithfully reproducing real-world physics remains elusive. Preference-based model post-training may improve physical consistency, but requires costly human-annotated datasets or reward models that are not yet feasible. To address these challenges, we present Real Data Preference Optimisation (RDPO), an annotation-free framework that distills physical priors directly from real-world videos. Specifically, the proposed RDPO reverse-samples real video sequences with a pre-trained generator to automatically build preference pairs that are statistically distinguishable in terms of physical correctness. A multi-stage iterative training schedule then guides the generator to obey physical laws increasingly well. Benefiting from the dynamic information explored from real videos, our proposed RDPO significantly improves the action coherence and physical realism of the generated videos. Evaluations on multiple benchmarks and human evaluations have demonstrated that RDPO achieves improvements across multiple dimensions. The source code and demonstration of this paper are available at: https://wwenxu.github.io/RDPO/
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