# What does the 2024 ICAT-EGVE poster map between FACS action units and ARKit blendshapes, in which direction, and what does it measure?

- id: art_01M5MS10CCEDA5GTVH3FQ5EYX8
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T07, T15 · tags: none
- updated: 2026-10-23T16:20:40.421Z
- url: https://veyrul.com/articles/art_01M5MS10CCEDA5GTVH3FQ5EYX8

The 2024 EGVE poster learns ARKit's 52 blendshape weights from 17 OpenFace action-unit intensities with a GRU, trained on 3,780 videos of nine Japanese speakers reading emotion-related sentences, and reports R2 about 0.75 with activation precision about 0.90 and recall about 0.85; it maps AUs to blendshapes for speech, so it is not a general AU-name lookup table and its population and task are narrow.

The 2024 EGVE poster learns ARKit's 52 blendshape weights from 17 OpenFace action-unit intensities with a GRU, trained on 3,780 videos of nine Japanese speakers reading emotion-related sentences, and reports R2 about 0.75 with activation precision about 0.90 and recall about 0.85; it maps AUs to blendshapes for speech, so it is not a general AU-name lookup table and its population and task are narrow.

Curator qualifications: Poster read 2026-10-22; model not run. Single small dataset (nine participants, one language); no manual FACS coding comparison.

Limitations: Poster read 2026-10-22; model not run.

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

```json
{
  "citations": [
    {
      "url": "https://diglib.eg.org/bitstreams/b0754f8b-5151-42c0-b3d4-89047e031724/download",
      "note": "diglib.eg.org ICAT-EGVE 2024 poster PDF"
    },
    {
      "url": "https://diglib.eg.org/handle/10.2312/egve20241392",
      "note": "diglib.eg.org ICAT-EGVE 2024 poster PDF"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] The 2024 EGVE poster learns ARKit's 52 blendshape weights from 17 OpenFace action-unit intensities with a GRU, trained on 3,780 videos of nine Japanese speakers reading emotion-related sentences, and reports R2 about 0.75 with activation precision about 0.90 and recall about 0.85; it maps AUs to blendshapes for speech, so it is not a general AU-name lookup table and its population and task are narrow. · locator: diglib.eg.org ICAT-EGVE 2024 poster PDF · source: https://diglib.eg.org/bitstreams/b0754f8b-5151-42c0-b3d4-89047e031724/download

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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