PROMPT RADAR / VIDEO视频叙事
Nike spends $2M on a stadium shoot for a 10-second hero shot. 40-person
Nike spends $2M on a stadium shoot for a 10-second hero shot. 40-person crew. 3 days of permits. 2 weeks in post. Stunt doubles. Insurance paperwork thicker than the script. This creator generated the entire sequence on
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编辑先看
它为什么值得进入灵感库
这条内容值得留下的,不是表面的风格词,而是它提供了一条可以继续实验的画面路径。 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
Nike spends $2M on a stadium shoot for a 10-second hero shot. 40-person crew. 3 days of permits. 2 weeks in post. Stunt doubles. Insurance paperwork thicker than the script. This creator generated the entire sequence on a laptop for under $80. A guy sprints across a stadium roof at night, leaps off the edge, free-falls into a sold-out arena, and lands on a giant football in the center of the pitch. One continuous shot. No crane. No drone operator. No safety harness review board. The toolchain behind this clip: > Kimi K3 wrote the full shot sequence: rooftop sprint, jump arc, mid-air hang, landing impact, crowd reaction timing > Kling 3.0 generated the fluid body physics and night lighting on the roof > Seedance 2.5 rendered the free-fall, wind on the shirt, and the ball deformation on impact > ElevenLabs produced the ambient stadium atmosphere and crowd noise > CapCut handled speed ramps, color grade, and final export A traditional production house would need a stadium rental at $150K, a stunt coordinator, a medical team on standby, and 6 months of liability clearance. This entire sequence was prompted, rendered, and exported between dinner and sleep. AI didn't replace the stunt double. It replaced the entire stadium booking.
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:Nike spends $2M on a stadium shoot for a 10-second hero shot. 40-person crew. 3 days of permits. 2 weeks in post. Stunt doubles. Insurance paperwork thicker than the script. This creator generated the entire sequence on
- 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 / Kling / Veo。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