微剪辑：电影中细粒度剪辑的计算支持
Micro-Edits: Computational Support for Fine-Scale Edits in Film
Nhan Tran, Hijung Valentina Shin, Abe Davis, Mackenzie Leake
AI 速览：该工作通过专业剪辑师的形成性研究理解细粒度剪辑（micro-edits）的结构，提出数学形式化，并开发基于AI与优化的工具以简化其探索与制作。
In film editing, every frame matters. While macro, content-level edits shape the high-level narrative of a film, small local edits often control timing and direct visual attention. For example, editors can adjust the precise timing of clips to control the length of a meaningful silence, or cut to shots that highlight a specific character's reactions to important moments. These small, detailed edits are slow and tedious to make in standard editing tools because users have to map high-level narrative goals to low-level frame-by-frame adjustments. At the same time, these fine-scale adjustments, which we call micro-edits, can be nuanced and expressive, with different choices serving different creative goals. In this work, we start by conducting a formative study with professional editors to understand the structure of micro-edits and how they are used. We then derive a mathematical formalization for this structure to develop tools that leverage AI and optimization to make exploring micro-edits simpler and more efficient for users. Finally, we validate our tools in multiple user studies and a wide range of video examples.