Physion-Eval: Evaluating Physical Realism in Generated Video via Human Reasoning
Video generation models are increasingly used as world simulators for storytelling, simulation, and embodied AI. As these models advance, a key question arises: do generated videos obey the physical laws of the real world? Existing evaluations largely rely on automated metrics or coarse human judgments such as preferences or rubric-based checks. While useful for assessing perceptual quality, these methods provide limited insight into when and why generated dynamics violate real-world physical constraints. We introduce Physion-Eval, a large-scale benchmark of expert human reasoning for diagnosing physical realism failures in videos generated by five state-of-the-art models across egocentri...
| Authors | Qin Zhang, Peiyu Jing, Hong-Xing Yu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
Models and datasets referencing this paper
- PhysionLabs/Physion-EvalDataset
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