AI 视频提示词生成器
给 Veo、Sora、Runway 和 Kling 的电影感提示词
像导演给摄影指导讲戏那样描述你的镜头：主体、动作、取景、运镜、光。这个免费生成器会把这些选择拼成一段结构化的提示词，让文生视频模型稳定地读懂它，而不是一串靠猜堆出来的形容词。
AI 视频模型对按固定顺序排列的真实电影语法反应最好：景别、主体、动作、场景、运镜、光、镜头质感、风格、情绪。这正是这个工具搭出来的结构，输出可直接复制的文本或 JSON。
提示词拼装是确定性的，且完全在你的浏览器里运行。你输入的内容不会被发送到任何服务器。
挑好取景、运镜和质感。不需要的留空，提示词会更精简。
你的提示词
在上面描述一个主体，你的提示词就会在这里拼出来。
工作原理
生成器按文生视频模型最容易读懂的顺序排列你的选择：先取景，再主体和动作，然后是场景、运镜、光、镜头、风格和情绪。每个选项都来自真实的电影摄影词汇，和镜头表上会用的景别与运镜是同一套，模型收到的于是是指令，而不是气氛形容词。JSON 标签页把同一个镜头输出为结构化字段，方便接进流水线和批量流程。
怎么写真正有效的 AI 视频提示词
AI 视频最常见的失败不在模型，而在提示词：一堆风格形容词，没有清晰的主体，没有具体的动作，也没有关于摄影机的指令。模型会用自己的想象填满每一个没说清的空档，所以每次生成的结果都在漂。
有力的提示词读起来像镜头描述，不像情绪板。一个主体、一个摄影机拍得下来的动作、一种取景、一次运镜。「一个饱经风霜的侦探在湿透的小巷里点燃一支烟的近景，缓慢推进，霓虹夜光」在所有主流模型上都胜过「氛围感黑色电影，4K，杰作」。
镜头之间的一致性来自重复，不是运气。把主体和场景的描述逐字锁死，在这个序列的每一条提示词里重复使用，只改镜头语法。用 AI 做电影的人，就是这样让一个角色从远景到近景都还认得出来的。
适合谁
- 用 AI 做电影的人 为整个序列生成一组彼此匹配的提示词，而不是凭记忆一条条即兴。
- 做预演的导演 为同一个瞬间生成远景、中景和近景，在真正开拍前测试镜头覆盖。
- 内容创作者和工作室 拿到可复用、商业可用的镜头提示词，不必再去零散的论坛帖子里学那些提示词玄学。
AI Video Prompt Generator: the complete guide
It assembles a structured cinematic prompt from real film language: shot size, camera movement, lighting, lens character, style, and mood, in the order text-to-video models parse best.
For this workflow, the central problem is clear: most AI video prompts are improvised strings of adjectives, so results drift between generations and never match the shot the creator actually imagined. Left unresolved, this creates downstream friction and slower decisions. The practical target is consistent, film-grammar-correct prompts that tell Veo, Sora, Runway, Kling, or Seedance exactly which shot to build.
Limitation to keep in mind: It builds the prompt, not the video: each model still interprets language differently, and character or location consistency across shots requires a workflow, not a single prompt.
Advanced workflow: Serious AI filmmakers keep a prompt sheet per scene, lock recurring subject and setting descriptions word-for-word across shots, and vary only the shot grammar between generations.
Step-by-Step Workflow
- Describe your subject and action in plain, concrete language: what a camera would actually see.
- Pick the shot size and camera movement the moment needs, the same way you would brief a DP.
- Choose lighting, lens character, style, and mood, then copy the assembled prompt or its JSON form.
- Reuse identical subject and setting wording across every shot in the scene to protect continuity.
Use Cases By Profile
- AI filmmaker: generate a matched set of prompts for a sequence instead of improvising each clip.
- Director: previsualize a scene's coverage by prompting the wide, medium, and close-up of the same moment.
- Content creator: get repeatable commercial-grade shots without learning prompt folklore from scattered threads.
Common Mistakes To Avoid
- Stacking ten style adjectives instead of one clear subject, one action, and one camera instruction.
- Changing the subject description between shots and wondering why the character's face drifts.
- Prompting emotional abstractions instead of physical, filmable descriptions of what is in frame.
Professional Best Practices
- One prompt, one shot: describe a single camera setup, not a whole scene of coverage.
- Concrete beats abstract: 'rain dripping from a fire escape' outperforms 'moody atmosphere'.
- Keep a locked description block for each recurring character and paste it verbatim into every prompt.
Treat this tool output as a decision support layer, not a replacement for authorship. Great scripts are remembered for specific choices, emotional precision, and clarity of dramatic movement. Tools help by removing noise so your energy can go where it matters: character, conflict, escalation, and payoff. If you review outcomes after each pass and keep an explicit log of accepted changes, your workflow becomes faster and more predictable from draft to draft. That consistency is exactly what professional collaborators value: fewer surprises, clearer rationale, and a script that evolves with intent.
Extended FAQ
What makes a good AI video prompt?
One clear subject, one concrete action, one camera instruction, then lighting and style. Models parse structured film grammar far better than a pile of adjectives, which is exactly what this generator assembles.
Do prompts work the same on Veo, Sora, Runway, and Kling?
The core structure transfers well across all major models, but each interprets language slightly differently. The generated prompt is a strong starting point; expect one or two iterations per model to dial in the result.
How do I keep a character consistent across AI video shots?
Lock the character's physical description word-for-word and reuse it in every prompt. Consistency comes from repeating identical wording across shots, which is easier when prompts come from a structured tool instead of memory.
Should I use text or JSON prompts?
Most models take natural-language prompts, which is the default output here. JSON is useful for structured pipelines, batch workflows, and models or tools that accept field-based input.
Why do my AI video results look different every generation?
Vague prompts leave the model free to reinvent everything. The more concretely you specify subject, action, framing, and lighting, the narrower the model's freedom and the more repeatable the result.
Can I use these prompts for a full AI short film?
Yes, treat each prompt as one shot in your shot list. Plan the sequence first, keep subject and setting descriptions locked across prompts, and generate shot by shot the way a production shoots coverage.
常见问题
能。它生成的提示词结构（取景、主体、动作、场景、运镜、光、风格）可以在所有主流文生视频模型之间通用。每个模型对语言的理解略有差异，所以每换一个模型都要预留一轮微调。
不是。它用真实的电影语法，在你的浏览器里确定性地拼装你的选择，不调用任何 API。同样的输入永远得到同样的提示词，而这正是镜头之间保持一致所需要的。
文本适用于所有主流模型的提示词输入框。JSON 适合结构化的流水线、批量生成，以及那些按字段接收输入的工具。
在这个序列的每一条提示词里，逐字复用同一段主体描述，两次生成之间只改镜头类型、运镜和动作。
模糊的提示词让模型可以自由重新发明所有没被交代的东西。你把主体、动作、取景和光说得越具体，它的自由度就越窄，你的结果也就越可复现。
