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FACS Action Unit Reference Grid

FACS Action Unit Reference Grid AI Image Prompt

Prompt

Type: FACS facial action coding system reference grid poster Title: FACS Reference Grid — 48 Action Units (v3 Step 2/3 fix) Subtitle: OLD algorithm woman, Cafe Window | Model Name: banana 2 | eyes v3.1, flash mug-preview Overall style: dark technical contact-sheet layout, small white sans-serif typography, thin grid gutters, photographic thumbnails, warm cafe-window lighting, muted black and charcoal UI background Subject: young woman with long dark hair holding a beige ceramic cup near her mouth Setting: warm cafe window interior with vertical window frames and golden daylight Face treatment: each thumbnail shows the same portrait composition with a large soft-edged beige rectangular anonymization mask covering the central face area while hair, hands, cup, neck, and background remain visible Layout: canvas is portrait poster, approximately 4:5 aspect ratio, black background; header is two-line header at top left, compact and technical; grid has 49 total tiles, 48 filled action units, 1 empty tile, 7 columns, 7 rows; thumbnail style: each tile has a photographic image on top and a dark caption bar below with a numeric index, AU code, and action label; empty tile: bottom-right tile is blank dark gray with tiny text reading missing and a label line 49. --- Action units: (48 items, each with index, code, and label, omitted for brevity) Generation instructions: Create a consistent reference grid using the same subject, camera angle, cup, lighting, and background in every filled tile. Vary only the facial action or head/eye direction indicated by each label, even though the face is mostly hidden by the anonymization mask. Keep labels crisp and legible, with exact AU codes and numbering.

AI Image Content Analysis

Content: Structured poster layout: subject (parameterized person/scene), grid (7x7 with 48 action units + 1 empty), photographic thumbnails + caption bars, dark technical style, warm lighting, face anonymization mask

Pros: Highly detailed layout and generation instructions ensure consistency; parameterized variables facilitate reuse and adjustment

Cons: Prompt is too long and complex, may exceed AI execution capability; strong reliance on precise grid arrangement prone to layout deviations

Reference image: No obvious reference-image dependency

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