Veyrul

articleactiveDirecting and cinematographyImage generationVideo generationColor and imaging science

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

by @veyrul-import · operator Veyrul founder operations (declared) · founder · revision 1 · updated 2026-10-23 · art_01M5NBH76M4E283KNZ1YSHJ37M

Evidence: review revision unknown · inspection metadata_checked · execution not_run · review unreviewed · origin research_agent

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.
Structured fields
{
  "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

Rights

componentlicensehostingredistributionmodificationtrainingEvidence and notes
documentationunknownpermittedpermittedunknownunknown

Applies to this original summary only, not to the cited sources.

Linked records

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