# How does PhyGenBench / PhyGenEval check physical commonsense in text-to-video models, how well does it track humans, and what did it find about scaling and prompt engineering?

- id: art_01M5MB3MEXJVHTQMT2MEM66MKY
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T06, T20 · tags: none
- updated: 2026-10-23T12:17:26.486Z
- url: https://veyrul.com/articles/art_01M5MB3MEXJVHTQMT2MEM66MKY

PhyGenBench (Oct 2024) has 160 text prompts covering 27 physical laws in four domains (mechanics, optics, thermal, material properties) and PhyGenEval, a three-stage check that uses a VLM and an LLM-generated question set: key physical phenomena detection on retrieved key frames, physics order verification across first/key/last frames, and an overall naturalness grade. On 64 prompts and 8 models, three annotators rated 512 videos; PhyGenEval correlates 0.81 overall with humans and the authors show VideoPhy, VideoScore and DEVIL rate physically wrong videos as correct. The best model, Gen-3, reaches 0.51 and optics is easiest; the authors report scaling and prompt engineering do not fix dynamic phenomena.

PhyGenBench (Oct 2024) has 160 text prompts covering 27 physical laws in four domains (mechanics, optics, thermal, material properties) and PhyGenEval, a three-stage check that uses a VLM and an LLM-generated question set: key physical phenomena detection on retrieved key frames, physics order verification across first/key/last frames, and an overall naturalness grade. On 64 prompts and 8 models, three annotators rated 512 videos; PhyGenEval correlates 0.81 overall with humans and the authors show VideoPhy, VideoScore and DEVIL rate physically wrong videos as correct. The best model, Gen-3, reaches 0.51 and optics is easiest; the authors report scaling and prompt engineering do not fix dynamic phenomena.

Curator qualifications: Counts: '160' 7, '27 physical laws' 2, '0.81' 8, '512' 2, 'Gen-3' 13, 'three annotators' 1.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2410.05363v1",
      "note": "PhyGenBench arXiv 2410.05363v1 PDF"
    },
    {
      "url": "https://github.com/OpenGVLab/PhyGenBench/blob/f8642cb796f3bcb01f0b7c1b2ec53b75d357c739/README.md",
      "note": "PhyGenBench arXiv 2410.05363v1 PDF"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] PhyGenBench (Oct 2024) has 160 text prompts covering 27 physical laws in four domains (mechanics, optics, thermal, material properties) and PhyGenEval, a three-stage check that uses a VLM and an LLM-generated question set: key physical phenomena detection on retrieved key frames, physics order verification across first/key/last frames, and an overall naturalness grade. On 64 prompts and 8 models, three annotators rated 512 videos; PhyGenEval correlates 0.81 overall with humans and the authors show VideoPhy, VideoScore and DEVIL rate physically wrong videos as correct. The best model, Gen-3, reaches 0.51 and optics is easiest; the authors report scaling and prompt engineering do not fix dynamic phenomena. · locator: PhyGenBench arXiv 2410.05363v1 PDF · source: https://arxiv.org/abs/2410.05363v1

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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