{"schema":"record/v1","id":"art_01M5NBH76M4E283KNZ1YSHJ37M","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How do creators make AI video look like real camera footage (prompt formula, imperfection, camera behaviour) and pick models for realism?","summary":"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...","status":"active","body":{"text":"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.\n\nDan 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.\n\nCurator 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.\n\nLimitations: 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.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://www.youtube.com/watch?v=2hJsvDSPWYM","note":"3:01; 6:02-9:04; 12:05; 15:06-18:07"}]},"categories":["T02","T05","T06","T12"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"metadata_checked","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://www.youtube.com/watch?v=2hJsvDSPWYM","claim":"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","classification":"sourced_fact"}],"execution_record_ids":[]},"rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-b06591e56cadf65e23375a1f2468b49025bf0f76/record.json#p-b06591e56cadf65e23375a1f2468b49025bf0f76/record","import_batch_id":"pipe-85056c4a6f7cbdaa5f771629344d65addd004364","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-85056c4a6f7cbdaa5f771629344d65addd004364","original":{"body":{"text":"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.\n\nDan 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.\n\nCurator 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.\n\nLimitations: 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.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://www.youtube.com/watch?v=2hJsvDSPWYM","note":"3:01; 6:02-9:04; 12:05; 15:06-18:07"}]},"tags":[],"type":"article","links":[],"title":"How do creators make AI video look like real camera footage (prompt formula, imperfection, camera behaviour) and pick models for realism?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"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...","evidence":{"claims":[{"url":"https://www.youtube.com/watch?v=2hJsvDSPWYM","claim":"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","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"metadata_checked","execution_record_ids":[]},"language":"en","categories":["T02","T05","T06","T12"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":[],"confidence":"Sponsored; model judgments subjective, single prompts; some model names (GPT Image 2.5 \"Flare\") unverified.","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.","Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. Original evidence pins: V2: no byte pin recorded (unknown)"],"observed_at":"2026-10-23T11:22:33.000Z","source_pins":[{"locator":"3:01; 6:02-9:04; 12:05; 15:06-18:07","source_id":"youtube-2hJsvDSPWYM","version_id":"ver:b2006791abb19f609a251751","observed_at":"2026-10-23T11:22:33.000Z","canonical_url":"https://www.youtube.com/watch?v=2hJsvDSPWYM","version_sha256":"d70c6d9bfc26af721865893d6572d305f624640b149eca01725aacd58f87c41a"}],"candidate_id":"C3-B361-02","claim_status":"source_fact","rights_basis":"Original curator/discovery summary with short quotations; public sources; source rights unchanged and not cleared for reuse of the source itself.","qualification":"shared_operator_desk_qa","supersedes_sha256":null}},"visibility":"public","attribution":{"origin":"research_agent","upstream_url":null,"imported_from":null,"upstream_owner":null,"import_batch_id":null}},"observed_on":null,"original_id":"p-b06591e56cadf65e23375a1f2468b49025bf0f76/record","source_file":"records/p-b06591e56cadf65e23375a1f2468b49025bf0f76/record.json","research_side":{},"source_schema":"create-record-input/v1","original_sha256":"67aa3fede41e9a069401ba6b74fa0e7e8def0eee379d1a6562d0240781c16096","unmapped_fields":[]}],"publication":{"rule":"item_visibility","policy":"l05-publication/1","visibility":"public"}},"curation/v1":{"curator":"CURATOR-A-20261021","revision":1,"topic_ids":[],"confidence":"Sponsored; model judgments subjective, single prompts; some model names (GPT Image 2.5 \"Flare\") unverified.","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.","Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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