# What is EWMBench, what exactly does it measure (scene, motion, semantics), what inputs does it need, and how do I run it?

- id: art_01M5MC5MRPSTTB4RFTYGQZXQG7
- 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, T15, T20 · tags: none
- updated: 2026-10-23T12:36:00.913Z
- url: https://veyrul.com/articles/art_01M5MC5MRPSTTB4RFTYGQZXQG7

EWMBench (AgiBot, arXiv v2) scores image-text-to-video models on robot manipulation: given an initial scene image, an instruction and optionally a 6D trajectory, the model generates frames that are compared with ground-truth AgiBot-World episodes. It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). It is robot-specific, fixed-viewpoint and needs a ground-truth trajectory per case.

EWMBench (AgiBot, arXiv v2) scores image-text-to-video models on robot manipulation: given an initial scene image, an instruction and optionally a 6D trajectory, the model generates frames that are compared with ground-truth AgiBot-World episodes. It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). It is robot-specific, fixed-viewpoint and needs a ground-truth trajectory per case.

Curator qualifications: All 8 excerpts located; sample-count and EWMBM/EWMBench naming conflicts kept.

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/html/2505.09694v2",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/AgibotTech/EWMBench/tree/3a5531c0e6b6be51431251ed5617b9e48c852098",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] EWMBench (AgiBot, arXiv v2) scores image-text-to-video models on robot manipulation: given an initial scene image, an instruction and optionally a 6D trajectory, the model generates frames that are compared with ground-truth AgiBot-World episodes. It measures scene consistency (fine-tuned DINOv2 patch similarity), end-effector motion (Hausdorff, normalised DTW and a velocity/acceleration Wasserstein term from a fine-tuned YOLO-World detector), and semantics (a Qwen2.5-VL caption compared by BLEU and CLIP score, a diversity term, and a logic check). It is robot-specific, fixed-viewpoint and needs a ground-truth trajectory per case. · locator: candidate evidence excerpts · source: https://arxiv.org/html/2505.09694v2

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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