PROMPT RADAR / VIDEO视频叙事
This might be the best synthetic performance I have got from a video mod
This might be the best synthetic performance I have got from a video model. We can generate 30-second shots and create near seamless extensions (with a bit of post blending) for far longer sequences, powered by @seedance
@Uncanny_Harry 580 热度 1.3 万 浏览
编辑先看
它为什么值得进入灵感库
先别急着照抄整段 Prompt:真正有用的是主体、空间和镜头之间的约束关系。 把一段描述拆成起始状态、动作变化、镜头响应和结束画面,连续性比形容词更重要。
01 / 原始方法
动作与镜头 Prompt
This might be the best synthetic performance I have got from a video model. We can generate 30-second shots and create near seamless extensions (with a bit of post blending) for far longer sequences, powered by @seedance 2.5. I set out to test this out by making the scene below all-in-one shot. I wrote the script, built the character references in Seedream and then broke the script down to two 30 second prompts. The prompts were really detailed with pauses, gestures and emotional shifts and the model delivered on all of them. I was really impressed with the performance and voices which were generated in the model. Previously, AI film scenes had to be composed of short cuts, now we have options on pacing, and nuance... we can let our scenes breathe. All this done with only around 3 rolls per take, we have left the slot machine age of AI video Gen behind and are now in the age of control. Next for me will be testing real actors performance as a reference to drive the generation. Seedance 2.5 API is about to launch on @BytePlusGlobal , and @lumina_ai_aiart will also roll out Seedance 2.5 soon.
02 / 复刻路径
不要一次改完所有变量
用 Seedance 复刻时,建议先单独生成稳定首帧,再把动作拆成“开始、变化、收束”三段;每段只保留一个主要运动。
- 主体:This might be the best synthetic performance I have got from a video model. We can generate 30-second shots and create near seamless extensions (with a bit of post blending) for far longer sequences, powered by @seedance
- 视觉方向:实时 AI 视频案例;查看原帖媒体与作者说明
- 镜头与构图:按原帖镜头流程执行;查看原帖媒体与工作流
- 交付约束:auto,保留原作者方法与归属;复刻时避免水印、低清、畸变、主体漂移和无关文字。
负向约束
保留原作者方法与归属;复刻时避免水印、低清、畸变、主体漂移和无关文字。
模型建议
Seedance / Veo / Kling。先用原始 Prompt 建立基准结果,再根据模型对自然语言、镜头运动或文字排版的能力做局部改写。
来源与编辑原则