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arXiv · 2609.30623

CraftTrace: Unflattening Videos into Malleable, Creation-Inspired Structures for Generative Editing

Boyu Li·Yuqian Zhou·Duotun Wang·Ding Li·Zhe Lin·Nanxuan Zhao·Zeyu Wang·Lin-Ping Yuan·Hongbo Fu
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Abstract

Recent generative video editing models enable video content modification (e.g., changing a character) but target short clips. Extending them to full multi-shot videos requires tedious work to locate relevant content across shots, segment it into clips, craft context-aware editing prompts for each clip, and repeatedly articulate complex editing intent. To address this, we explore an interaction paradigm for editing through underlying video structures (e.g., scripts, scenes, characters, shots, and their relationships). We present CraftTrace, an interactive prototype that transforms a video into a malleable, multilevel structure for generative editing. Users work in task-centric workspaces to modify elements or reshape relationships, while an AI agent translates and propagates changes across the video. A user study and expert review show that this structure helps users understand videos, formulate and refine editing intent, and explore alternatives, supporting rapid prototyping during early-stage exploration and full video post-production.

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Boyu Li, Yuqian Zhou, Duotun Wang, Ding Li, Zhe Lin, Nanxuan Zhao, Zeyu Wang, Lin-Ping Yuan, Hongbo Fu. 2026-09-24. CraftTrace: Unflattening Videos into Malleable, Creation-Inspired Structures for Generative Editing. https://arxiv.org/abs/2609.30623

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