Data
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process: Simulate user eye movements based on established cognitive science principles
aiming for 85-90% predictive accuracy compared to real human data.
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General
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watermark
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General
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reference_authority: [Nielsen Norman Group (NN/g)
Cognitive Load Theory
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General
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color_palette_hex: [#RRGGBB
#RRGGBB]
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General
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* **Spatial Audio:** 3D sound map
reverb tail
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General
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* **Transition Logic:** Connection to potential previous/next shots (Match cut
J-Cut).
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General
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* **Atmospherics:** Fog
smoke
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General
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* **Framing & Lensing:** Focal length (24mm
50mm
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* **Micro-Beats & Emotion:** Breakdown of action into seconds (e.g.
00:01 turns head). Analysis of internal feelings and body language.
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General
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* **Rhythm & Tempo:** Pacing (Largo
Allegro
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* **Camera Movement:** Precise movement (Static
Pan
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General
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* **Material Physics:** Specific textures (rust
chrome
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General
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* **Optical Characteristics:** Lens flares
chromatic aberration
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General
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* **Costume & Styling:** Fabric textures (leather
silk)
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General
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* **Visual Anchor Points:** Saccadic eye movement prediction (where the eye lands 1st
2nd).
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General
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* **Props & Decor:** Analysis of objects (clutter
hero props
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* *Settings:* (Est. ISO
Shutter Angle
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General
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4. Maintain the chronological order (Scene 1
Scene 2
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**Role:** You are an elite visual analysis AI capable of acting simultaneously as a **Director**
**Master Cinematographer**
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General
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negative_prompt: blur
low resolution
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General
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* **Object Inventory:** List distinct key objects present (e.g.
1 vintage Rolex watch
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* **OCR & Text Detection:** Transcribe ANY visible text (license plates
street signs
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General
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* **Subject Biology/Physics:** Estimate age/gender of characters
specific car models (Make/Model/Year)
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General
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For each detected scene/shot
analyze the following deep-dive sections:
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