Chinese AI Video Prompt Builder

Structure is what makes a text-to-video prompt reliable

When you prompt a Chinese text-to-video model — Jimeng (即梦/Seedance), Kling (可灵), Hailuo (海螺), Vidu or Tongyi Wanxiang (通义万相) — the look of the clip depends far more on how you structure the description than on any single clever phrase. A structured prompt names the subject and scene, states the action, fixes the camera movement, sets the shot size, chooses the visual style, defines the lighting and mood, sets the duration and pacing, and lists what to exclude. This builder keeps that structure for you: fill the fields, and it joins them into a clean prompt that opens with the subject and scene, followed by clearly headed sections, each prefixed with a Markdown-style heading the model can read at a glance, ready to paste into any video model. The result is the kind of prompt a careful video director would assemble by hand, only built in seconds.

The foundation is the subject and scene. "A woman in a red trench coat on a neon-lit, rain-soaked street" gives the model a concrete frame to work from — appearance, clothing and environment all in one line — so it improvises less on the details that matter. After the subject, the action and the camera movement do the heavy lifting: the action says what happens in the shot, and the camera move (push in, pull out, pan, track, orbit, follow) directs exactly how the viewer's eye travels. Then the shot size — close-up, medium, wide — decides how large the subject sits in the frame, which often defines the feel of a shot more than any adjective. A good rule of thumb is to make each field concrete: instead of "make it dynamic", say "slow push-in on a medium shot as she turns toward camera".

Camera, shot size and exclusions separate a clip from a usable shot

The fields people skip and regret are style, lighting and the exclusion list. The style keeps the look consistent — photoreal, cinematic, anime, 3D render; the lighting and mood (golden hour, cool night, soft light, neon) are frequently the single biggest gap between a cinematic frame and a flat one; and the exclusions — "no on-screen text, no distorted hands, no extra people" — quietly cut the artefacts that ruin an otherwise good clip. None of this fights the model; it focuses it. Keeping the duration realistic matters too: most Chinese video models render roughly five to ten seconds per shot, so describing one coherent action almost always holds together better than cramming several beats into a single generation.

Because the output is structured plain text, the same prompt is portable across every major Chinese video model and works just as well on Runway, Sora or Pika. Write it in Chinese when you want the most natural results from a Chinese model; the structure travels regardless of language. And because the whole tool runs locally in your browser, you can iterate freely — change one field, copy again, and re-render — without anything you type ever leaving your device, being sent to a model, or being stored. Treat the first prompt as a draft: generate it, see where the shot drifts, and tighten the matching field — one variable at a time so you can tell which change did the work. Two or three rounds of that usually turn a rough clip into exactly the shot you wanted, and you keep a clean, reusable prompt at the end.