| title | Thinking on Shots: Consistent Multi-Shot Video Editing with Agentic Reasoning |
|---|---|
| authors | Chenyang Wu, Fuchen Long, Binyuan Huang, Xinlong Sun, Xi Chen, Chun-Le Guo, Chongyi Li |
| date | 2026-08-27 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.26809 |
| affiliation | Nankai University |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | While generative AI has significantly advanced video editing, existing methods primarily focus on single-shot or short video clips. Editing long videos with multiple instructions remains a formidable challenge. Naive chunking strategies, e.g., fixed-duration segmentation, often lead to entity fragmentation, severe editing hallucinations, and disrupted temporal continuity. To bridge this gap, we introduce the Multi-Instruction Multi-Shot Long-Video Editing (MMLVE) task, which is structured around three core objectives: Cross-Shot Editing Consistency (CSEC), Multi-Instruction Decoupling (MID), and Zero-Destruction on Spatiotemporal Structure (ZDSS). To tackle these three unique challenges, we introduce an agentic editing framework that leverages the synergy of Large Language Models (LLMs) and Vision-Language Models (VLMs) to achieve shot-level video decoupling and precise instruction parsing. Furthermore, to comprehensively evaluate this task, we construct MMLVE-Bench, which is an MMLVE-focused dataset characterized by complex real-world spatiotemporal dynamics, high-density heterogeneous instructions, and sparse, random entity distributions. Three MMLVE-focused evaluation metrics are further exploited to assess the quality of the editing results. Extensive experiments demonstrate that our MMLVE-Agent outperforms existing closed-source SOTA approaches (e.g., Seedance 2.0), successfully eliminating editing hallucinations, preserving cross-shot editing consistency, and attaining seamless spatiotemporal transitions. |
cite
@misc{wu2026thinking,
title={Thinking on Shots: Consistent Multi-Shot Video Editing with Agentic Reasoning},
author={Chenyang Wu and Fuchen Long and Binyuan Huang and Xinlong Sun and Xi Chen and Chun-Le Guo and Chongyi Li},
year={2026},
eprint={2608.26809},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.26809}
}