# What do the identity and consistency scores in video-generation papers (subject consistency, FaceSim, NexusScore, Inter-Sub.Con, ACP-Var, LLM-judge accuracy) actually measure, and what can each one miss?

- id: art_01M5MBY8DXR60BJGZ9WDCP89ZB
- 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: 2026-10-23T12:31:58.902Z
- url: https://veyrul.com/articles/art_01M5MBY8DXR60BJGZ9WDCP89ZB

The papers use at least six different 'consistency' numbers that are not interchangeable: VBench-style subject consistency (DINO similarity of adjacent frames inside one shot), face-embedding similarity (FaceSim, InsightFace/ArcFace) to a reference or between shots, NexusScore (detector-cropped subject versus reference by a retrieval model), Ref-Sub.Con versus Inter-Sub.Con (to the reference image versus between generated shots), anti-copy-paste scores (ACP-Var, CP-Rate), and VLM or human judgements (EntityBench same/different accuracy, PersonaShot MOS-aligned evaluators). Each can be high while identity is wrong: within-shot DINO scores ignore cuts, embeddings can reward generic faces, face scores ignore costume, and reference-fidelity scores can reward pasting the reference. Scores from different papers use different data, models, and settings and must not be compared across tables.

The papers use at least six different 'consistency' numbers that are not interchangeable: VBench-style subject consistency (DINO similarity of adjacent frames inside one shot), face-embedding similarity (FaceSim, InsightFace/ArcFace) to a reference or between shots, NexusScore (detector-cropped subject versus reference by a retrieval model), Ref-Sub.Con versus Inter-Sub.Con (to the reference image versus between generated shots), anti-copy-paste scores (ACP-Var, CP-Rate), and VLM or human judgements (EntityBench same/different accuracy, PersonaShot MOS-aligned evaluators). Each can be high while identity is wrong: within-shot DINO scores ignore cuts, embeddings can reward generic faces, face scores ignore costume, and reference-fidelity scores can reward pasting the reference. Scores from different papers use different data, models, and settings and must not be compared across tables.

Curator qualifications: All excerpts located; six non-interchangeable 'consistency' numbers explained.

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/2605.15199v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2505.20292v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2604.23789v3",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2608.16717v1",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] The papers use at least six different 'consistency' numbers that are not interchangeable: VBench-style subject consistency (DINO similarity of adjacent frames inside one shot), face-embedding similarity (FaceSim, InsightFace/ArcFace) to a reference or between shots, NexusScore (detector-cropped subject versus reference by a retrieval model), Ref-Sub.Con versus Inter-Sub.Con (to the reference image versus between generated shots), anti-copy-paste scores (ACP-Var, CP-Rate), and VLM or human judgements (EntityBench same/different accuracy, PersonaShot MOS-aligned evaluators). Each can be high while identity is wrong: within-shot DINO scores ignore cuts, embeddings can reward generic faces, face scores ignore costume, and reference-fidelity scores can reward pasting the reference. Scores from different papers use different data, models, and settings and must not be compared across tables. · locator: candidate evidence excerpts · source: https://arxiv.org/html/2605.15199v1

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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