# How do creators make AI video look like real camera footage (prompt formula, imperfection, camera behaviour) and pick models for realism?

- id: art_01M5NBH76M4E283KNZ1YSHJ37M
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=not_run, review=unreviewed
- categories: T02, T05, T06, T12 · tags: none
- updated: 2026-10-23T21:44:06.094Z
- url: https://veyrul.com/articles/art_01M5NBH76M4E283KNZ1YSHJ37M

Creator-reported: statements attributed to the creator(s) come from discovery's reading of auto-caption transcript excerpts; the curator checked the video metadata (title, date, length) and any cited documentation pages, not the spoken claims. Nothing was tested; promotional or sponsored framing applies where stated. Dan Kieft (2026-10-16, Higgsfield-sponsored): for stills, test the same prompt across several image models because each is trained differently (in his comparisons GPT Image 2.5 "Flare", FLUX 2 Pro, Nano Banana Pro/2.1, Seedream 5.0 and Higgsfield Soul each won some cases; FLUX 3 looked plastic for stills; text rendering failed on Soul 2 and FLUX 2 Pro). His "CTLT" prompt order: Camera (film stock/lens/framing, e.g. 35 mm film, 12 mm fisheye, tilted horizon, slight motion blur), Tone (mood/emotional energy, e.g. "we weren't meant to see this"), subject/action, Lighting (e.g. harsh on-camera flash), Texture (physical surface details); style labels such as disposable-camer...

Creator-reported: statements attributed to the creator(s) come from discovery's reading of auto-caption transcript excerpts; the curator checked the video metadata (title, date, length) and any cited documentation pages, not the spoken claims. Nothing was tested; promotional or sponsored framing applies where stated.

Dan Kieft (2026-10-16, Higgsfield-sponsored): for stills, test the same prompt across several image models because each is trained differently (in his comparisons GPT Image 2.5 "Flare", FLUX 2 Pro, Nano Banana Pro/2.1, Seedream 5.0 and Higgsfield Soul each won some cases; FLUX 3 looked plastic for stills; text rendering failed on Soul 2 and FLUX 2 Pro). His "CTLT" prompt order: Camera (film stock/lens/framing, e.g. 35 mm film, 12 mm fisheye, tilted horizon, slight motion blur), Tone (mood/emotional energy, e.g. "we weren't meant to see this"), subject/action, Lighting (e.g. harsh on-camera flash), Texture (physical surface details); style labels such as disposable-camera flash, low-quality camcorder at 1K resolution, iPhone selfie change identity; deliberate imperfection reads as real; save a liked look as a reusable LLM "MD skill" file. For video realism: Seedance 2.5 best but costly; FLUX 3 video gave ~80-90% for less; MiniMax H3 Max weaker without references; Seedance 480p draft can look more realistic. Human camera behaviours to prompt: handheld micro-shake reacting to events, crash zoom, voyeuristic framing, whip pan to an event, operator walking backwards, someone blocking the frame, exposure re-adjusting when moving indoors/outdoors, focus hunting. Social "contrast" ladder for ideas: subject in expected place (none) -> unexpected situation -> plus unexpected styling/attitude -> plus feeling.

Curator qualifications: Creator-reported: statements attributed to the creator(s) come from discovery's reading of auto-caption transcript excerpts; the curator checked the video metadata (title, date, length) and any cited documentation pages, not the spoken claims. Nothing was tested; promotional or sponsored framing applies where stated. All cited documentation excerpts located on the live pages by curator GET; video metadata checked where cited.

Limitations: Creator statements were read by discovery from auto-captions (ASR); not re-verified by the curator. Sponsored; model judgments subjective, single prompts; some model names (GPT Image 2.5 "Flare") unverified.

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

```json
{
  "citations": [
    {
      "url": "https://www.youtube.com/watch?v=2hJsvDSPWYM",
      "note": "3:01; 6:02-9:04; 12:05; 15:06-18:07"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] Dan Kieft (2026-10-16, Higgsfield-sponsored): for stills, test the same prompt across several image models because each is trained differently (in his comparisons GPT Image 2.5 "Flare", FLUX 2 Pro, Nano Banana Pro/2.1, Seedream 5.0 and Higgsfield Soul each won some cases; FLUX 3 looked plastic for stills; text rendering failed on Soul 2 and FLUX 2 Pro). His "CTLT" prompt order: Camera (film stock/lens/framing, e.g. 35 mm film, 12 mm fisheye, tilted horizon, slight motion blur), Tone (mood/emotional energy, e.g. "we weren't meant to see this"), subject/action, Lighting (e.g. harsh on-camera flash), Texture (physical surface details); style labels such as disposable-camera flash, low-quality camcorder at 1K resolution, iPhone selfie change identity; deliberate imperfection reads as real; save a liked look as a reusable LLM "MD skill" file. For video realism: Seedance 2.5 best but costly; FLUX 3 video gave ~80-90% for less; MiniMax H3 Max weaker without references; Seedance 480p draft can look more realistic. Human camera behaviours to prompt: handheld micro-shake reacting to events, crash zoom, voyeuristic framing, whip pan to an event, operator walking backwards, someone blocking the frame, exposure re-adjusting when moving indoors/outdoors, focus hunting. Social "contrast" ladder for ideas: subject in expected place (none) -> unexpected situation -> plus unexpected styling/attitude -> plus feeling. · locator: 3:01; 6:02-9:04; 12:05; 15:06-18:07 · source: https://www.youtube.com/watch?v=2hJsvDSPWYM

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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