--- name: video-cinematography description: Construct the actual generation prompt for an AI video/image shot -- the five-aspect formula (subject, motion, scene, spatial framing, camera), a precise camera-movement and lighting vocabulary, and keeping a character or subject visually consistent across shots. Use after video-storytelling has placed a beat in the narrative arc and video-model-selection has picked a model, and whenever a generated shot needs re-prompting because framing, motion, lighting, or identity drifted from the brief. --- # Cinematography and consistency for generated video A generation model responds to specific technical direction far better than a vague creative brief. This skill turns a storyboard beat into that direction. It is provider- and model-agnostic -- `video-storytelling` places the beat in the narrative arc, `video-model-selection` picks the model, `fal-ai`/`google-ai` make the call; this skill decides what you tell the model to do once you're ready to write the actual prompt. ## The self-contained-prompt test Before generating, check: could someone who has never seen the intended shot picture the subject, scene, motion, and camera work from the prompt text alone? If a human reader couldn't picture it from the words, a generation model will not render it either. Vague, mood-only language ("cinematic," "epic," "moody") fails this test even when it sounds evocative -- see "Describe the visual cause, not the emotion" below. ## The five-aspect prompt formula Video generation models reliably render a described subject and scene, but routinely fail on motion, spatial framing, and camera work when those are left implicit. Structure every generation prompt to fill all five slots explicitly, not just the ones that feel obvious: ``` Subject type + key visual attributes + how to tell apart from other subjects in frame Subject motion actions in temporal order; how subjects interact with objects and each other; group action if more than one Scene setting + time of day + scene dynamics -- list any on-screen overlays (titles, captions) separately, they are NOT part of scene depth (see below) Spatial framing shot size + where the subject sits in frame + depth (foreground/midground/background) + camera height relative to subject -- and how any of this changes during the shot Camera playback speed, then lens effects, then height, then angle, then focus/depth-of-field, then steadiness, then movement -- in that order ``` Shorter prompts leave the model more creative freedom; longer, fully-specified prompts give more control. Neither is universally right -- match the prompt's density to how much this specific shot needs to be controlled versus discovered, and check `video-model-selection`'s dated notes for how much prompt length a given model actually rewards (some plateau past ~250 words, others are built for 200-400 word structured prompts). ### Ordering multiple subjects or events When a prompt lists more than one subject or event, order them deliberately: **temporal order** when things happen in sequence ("first X enters, then Y reacts"), **prominence order** when timing isn't the point (people before objects, the largest/most-centered subject first, secondary subjects after). ## Shot composition: map the beat to the frame For every shot, resolve before generating: - **What the shot proves.** One shot, one idea -- match the visual change to the story beat it serves (see `video-storytelling`). A shot trying to prove two things usually proves neither clearly in a few seconds. - **Framing**: wide/establishing (location context), full/long (subject head-to-toe with environment), medium (waist-up, balances detail and context), medium close-up (chest-up, conversational), close-up (face or key object, emphasizes emotion or a specific proof point), extreme close-up (one isolated detail). Choose the tightest framing that still reads clearly. - **Composition**: rule-of-thirds subject placement, leading lines, negative space for text/overlay if the shot will carry on-screen copy, headroom appropriate to the framing. ## Camera vocabulary: precise, not evocative Name the exact technique rather than describing an effect and hoping the model infers it -- models respond far more reliably to named, correctly grouped camera language, and conflating two different primitives routinely produces the wrong result. ### Camera movement, grouped correctly Models frequently confuse camera translation, rotation, and lens-only changes when the prompt doesn't clearly separate them. Use the right group: - **Translation** (the camera physically moves through space): dolly in/out (along the lens axis), truck left/right (laterally), pedestal up/down (vertically). - **Rotation** (the camera stays in place and pivots): pan left/right (yaw), tilt up/down (pitch), roll clockwise/counter-clockwise (Dutch / Z-axis). - **Lens-only** (neither the camera nor its position changes): zoom in/out (focal-length change), rack focus / pull focus / focus tracking (focal-plane change). - **Hybrid / signature moves**: dolly zoom (the vertigo effect -- camera moves while the lens zooms to compensate, subject size stays constant while the background compresses or expands; a strong, specific effect, use only for genuine visual tension, not generic "cinematic" flavor), arc/orbit, crane/jib (vertical move, often combined with a pan -- reveals scale or transitions between a wide and a grounded view), whip pan, tracking/follow, handheld. - **Stillness**: static (strictly zero movement, zero focus change, zero zoom -- if any of those occur, it isn't static, name the actual primitive instead), micro-shake, locked-off. **dolly is not zoom** -- dolly is the camera physically moving; zoom is a focal-length change with the camera fixed. **pan is not truck** -- pan rotates in place; truck moves laterally. Models follow whichever word is present, so using the wrong one produces the wrong shot, not a close approximation. ### Camera height and angle State both independently -- height is where the camera physically sits (aerial, overhead, eye-level, hip-level, ground-level, water-level, underwater); angle is the camera's relationship to the subject (bird's-eye is strict top-down, not the same as merely "aerial" altitude; high angle looks down on the subject; level angle matches subject height; low angle looks up, reading as powerful/dominant; worm's-eye looks straight up; Dutch angle tilts the horizon, fixed or rolling, for unease or tension). ### Focus and depth of field Deep focus (everything sharp, foreground to background), shallow depth-of-field (subject sharp, background soft), extremely shallow (a razor focal plane), rack focus (a snap shift between two focal points mid-shot), pull focus (the same shift, slower and more gradual than rack), focus tracking (focus follows a moving subject). When focus changes during a shot, state both the starting and ending focal plane. ### Playback speed Time-lapse (much faster than real time), fast-motion (roughly 1-3x real time), slow-motion, stop-motion (discrete frame-by-frame movement), speed-ramp (a mix of fast and slow within one shot), time-reversed. These are distinct primitives, not synonyms for "fast" or "slow." ## Lighting and atmosphere State lighting direction as part of the prompt, not as an afterthought: - **Key light direction and quality**: front, side, back, or top; hard (sharp shadows, high contrast, dramatic) or soft (diffused, low contrast, flattering). Three-point lighting (key, fill, rim/back) is the reliable default for a clear, well-modeled subject when the brief doesn't call for something more stylized. Rembrandt lighting (a small light triangle on the cheek) is a specific, recognizable portrait look; film noir (deep shadow, stark highlight) and volumetric (visible light rays through fog or dust) are others worth naming explicitly rather than approximating. - **Color temperature and mood**: warm (golden hour, incandescent/tungsten) vs cool (overcast, moonlight, daylight/fluorescent) sets emotional register before a single word of copy appears on screen. - **Practical vs motivated light**: whether light sources are visible in frame (practicals: a window, a lamp, a neon sign) or implied off-frame -- affects how "real" vs "produced" a shot reads. - Keep lighting continuity across shots in the same scene/location unless a time or mood change is deliberate and telegraphed (e.g. a transition beat explicitly moving from day to night). Never state conflicting lighting in one prompt ("bright noon" plus "dark dramatic shadows") -- the model picks one and silently drops the other. ## Describe the visual cause, not the emotion Replace emotional or mood adjectives with the concrete visual detail that would actually cause that emotion in a viewer -- "inspiring," "epic," "powerful," and "moody" don't constrain a single pixel, so models render their own generic default for them: | Instead of | Write | |---|---| | "sad character" | "tears on the cheek, shoulders slumped, staring at an empty chair" | | "epic reveal" | "wide aerial pull-back; subject silhouetted against the rising sun" | | "cinematic mood" | "low-key Rembrandt key light, shadows lifted two stops, 35mm anamorphic, crushed blacks" | | "powerful music swell" | "music drops to silence at the cut, holds for 1.5s, returns with a low drum at half tempo" | This applies to narration/on-screen copy as much as to generation prompts -- a viewer reads "epic" as filler; they respond to what's actually on screen. ## Subject transitions When a shot introduces a new subject, removes one, or hands focus from one subject to another, name the transition explicitly rather than leaving it for the model to infer: - **Revealing**: a new subject enters or becomes visible mid-shot, by either subject movement or camera movement (a door opens, the camera pans to find them, fog clears). - **Disappearing**: an existing subject leaves frame or is removed (walks out, fades, is occluded). - **Switching**: focus jumps from one subject to another, typically via a cut, a rack focus, or a camera whip. - **Complex-alternating**: multiple subjects trade focus repeatedly within one shot (a debate, cross-cutting, ensemble action). Name the mechanism too ("by camera movement," "via rack focus") -- this is what actually unlocks reliable reveal-style camerawork rather than a random guess at how the transition should look. ## Overlays are not scene depth On-screen text, captions, titles, HUD elements, and watermarks are not part of a scene's foreground/midground/background depth axis -- list them separately with their exact content and placement, never as "overlay in the foreground." Exact text, logos, and prices belong in a deterministic overlay layer added in `video-editing`, not baked into the generation prompt -- models render text unreliably, and some (Google's Veo family specifically) may add unrequested subtitles by default for dialogue unless told not to. ## Define every recurring character before generating its first shot This is the hardest part of a multi-shot AI-generated production and the most common source of a viewer-visible defect (a character's face, outfit, or a product's exact appearance shifting between shots). Do not discover a character's appearance opportunistically shot by shot -- for every character, presenter, or product that recurs across more than one shot, produce both deliverables below **before generating any shot of that subject**, not after an inconsistency is already noticed: 1. **A written character spec.** One canonical, detailed description -- face, build, exact outfit, product's exact colors/markings -- saved once to `characters/.md` and referenced from `production.json`. Pick 3-6 specific, disambiguating visual attributes (e.g. "bald, blue arrow tattoo on forehead, orange-and-yellow robes") and reuse that exact phrase, verbatim, in every prompt for that subject. Pronouns and phrases like "the same character as before" do not carry identity across independent generations -- each generation is evaluated cold, as if it had never seen the earlier one. Do not let the description drift by paraphrasing it differently per shot. 2. **A generated character-sheet reference image.** Use `fal-ai` or `google-ai` (whichever the chosen model needs -- see `video-model-selection`) to generate one strong reference image of the character/subject from the spec, saved alongside its spec as `characters/.png`. Text descriptions alone drift across independent generations even with an identical prompt; a shared image reference does not. Both live in a `characters/` folder, and where that folder sits follows `video-creation`'s file-layout rule rather than being a third convention: in direct chat it is `work/productions//characters/`, namespaced per production because one project can hold several; running as a workflow stage it is `characters/` inside your own step folder. Keep the spec and its reference image side by side under the same character name -- a spec whose reference image has to be hunted for is a spec that gets paraphrased instead of reused. Call `show_character` as soon as both exist and **before generating any shot that uses them**. Every later shot is conditioned on that reference, so an unapproved face propagates through the whole piece and can only be undone by regenerating all of it -- this is the cheapest moment in the production to be told the character is wrong. Once both exist, every subsequent shot of that subject conditions on the same reference image and repeats the same spec phrase verbatim -- this is reference-image conditioning, the most reliable of the techniques below, and it only works if the reference was made first and reused deliberately, not regenerated per shot. ## Keep the whole character arc on one model and provider Once a model is chosen for a character (based on which one actually supports reference-image conditioning well -- see `video-model-selection`), commit to that same model and provider for every shot of that character in the production. This is a default to follow, not a preference: even with an identical reference image, different models and providers interpret and render a reference differently, so switching mid-arc routinely breaks consistency in a way that switching shots *within* one model, sharing its own reference, generally does not. Treat a mid-arc model or provider switch as a last resort requiring the user's explicit sign-off, not a routine production decision -- and if it happens, re-verify consistency against the reference image explicitly rather than assuming the new model's output matches by prompt alone. ## Other consistency techniques In addition to reference-image conditioning and the single-model/provider default above: - **Seed reuse where supported.** Some models expose a seed parameter that improves consistency across generations sharing it. This alone is usually not sufficient for a recognizable recurring character; combine it with the reference image, never use it as a substitute for one. Lock a seed once a shot's composition reads well, and reuse it for controlled variations of that same shot. - **Shot-to-shot anchoring.** For an image-to-video model, extracting a frame from an approved shot and using it as the reference/first-frame input for the next shot in the same sequence can carry appearance continuity forward through a scene, on top of the character-sheet reference. When chaining independently-generated clips this way, write the two adjoining prompts so the last described moment of clip N matches the first described moment of clip N+1 -- overlapping the description at the seam, not just the reference image, reduces visible discontinuity at the stitch point (see `video-editing`'s stitching guidance). - **Multi-shot generation in one call.** Some models (check `video-model-selection`'s dated notes) accept an explicit shot list inside a single generation call (`Shot 1 (...)`, `Shot 2 (...)`, etc.) and hold identity more reliably across shots generated together than across separate calls, since the identity anchor only has to be interpreted once per call rather than re-cold-started per shot. Prefer this for a tight sequence of the same subject when the model supports it. **When none of the above holds well enough**, treat that as a real constraint to report, not a defect to hide: tell the user the chosen model cannot reliably hold the character/subject consistent across this many shots, and offer the tradeoff (fewer shots of that subject, or accepting visible variation). Do not offer a model or provider switch as a casual equal option here -- per the rule above, that trades a known consistency problem for a second one and needs the user's explicit sign-off, not a silent default. ## Prompt iteration strategy 1. Start simple: subject, action, setting. See what the model produces before adding constraints. 2. Add one element at a time -- camera, then lighting, then style. Adding everything at once makes it hard to tell which change caused a result to improve or misfire. 3. If a shot misfires, strip back rather than piling on more adjectives: simplify the action, freeze the camera, remove one variable at a time. 4. For consistency across clips in one sequence, repeat the same style/lighting/grade description alongside the character reference, not just the subject description. ## Verify before calling a shot done After generating, check the result against what was asked for -- a successful API response is not proof the shot is right: - Does the framing/composition match what was specified, not a plausible alternative interpretation? - Does the camera movement match what was named, or did the model default to static/generic motion? - For a shot in an established sequence: does the subject's appearance actually match the reference/previous shots, checked by eye against the reference image or prior frame, not assumed from a matching prompt? Re-prompt with more specific, technically-named direction (not just a stronger adjective) when a shot misses -- most drift traces back to an under-specified prompt rather than an unreliable model. ## What to avoid | Don't | Why | Do instead | |---|---|---| | "Beautiful scene" | No visual information to render | "Wet cobblestone street, warm streetlamp glow reflecting in puddles" | | "Person moves quickly" | No visible, specific action | "She sprints three steps and vaults over the railing" | | Four or more simultaneous actions in one shot | Motion coherence tends to collapse | Split into a multi-shot sequence instead | | Readable text or logos inside the generated clip | Text rendering is unreliable across models | Keep exact text in the overlay layer (see "Overlays are not scene depth") | | Complex physics (explosions, crowds colliding) | Chaotic motion causes visible artifacts | Keep physics simple; walking/gesturing is reliable, destruction is risky | | Multiple characters speaking in one shot | Multi-person dialogue sync tends to break | One speaker per shot, or use reaction shots instead | | Everything specified at once on a first attempt | Hard to diagnose which element caused a miss | Layer in complexity one element at a time (see "Prompt iteration strategy") | ## Where this fits - Use `video-creation` to plan the shot list and own the overall brief. - Use `video-storytelling` to place each beat in the narrative arc before writing its shot. - Use `video-model-selection` to choose a model per shot. - Use this skill to turn the beat into the actual generation prompt -- camera, lighting, consistency -- before calling `fal-ai` or `google-ai`. - Use `video-editing` to assemble the results, `video-quality` before presenting any version as complete.