MoviEdit: Long-form Multi-Shot Video Editing Made Easy

Suhyeon Lee, Zhengang Li, Soo Ye Kim, Peihao Zhu, Jong Chul Ye, Chun-Hao Paul Huang, Duygu Ceylan, Yan Kang, Hyeonho Jeong

MoviEdit: Long-form Multi-Shot Video Editing Made Easy: 60 upvotes on Hugging Face Daily Papers, #4 of 46 papers on 2026-11-04. Day-by-day upvote history.

Upvotes over time

It had 60 upvotes by its first day, 60 upvotes by the next day, 60 upvotes by day 3, 60 upvotes by day 7, and has 60 now. It ranked #10 of 130 papers in its week and #10 of 130 in its month. Its code repository has 7 GitHub stars.

Long-form multi-shot video editing requires an intended edit to remain coherent across abrupt changes in scene, viewpoint, and subject appearance, while processing substantially longer visual context than conventional single-shot editors. We introduce MoviEdit, an end-to-end framework for scalable long-form multi-shot video editing that jointly addresses the challenges of data, modeling, and evaluation. To provide cross-shot supervision data at scale, we develop automatic data-generation pipelines spanning synchronized multi-view footage, real-world multi-shot videos, and generated multi-shot content, yielding 87K paired editing videos without manual annotation. Architecturally, MoviEdit employs a cascaded bidirectional-autoregressive design: a lightweight bidirectional stage first establishes a globally coherent editing draft over a spatiotemporally compressed representation, which is then autoregressively propagated and refined over the full video. We further introduce MoviEdit-Bench, a dedicated benchmark for systematic evaluation of long-form multi-shot video editing. Extensive experiments show that MoviEdit substantially outperforms existing open-source models and performs on par with industry frontier models across text-guided and image-guided editing. Visit our project page for complete results and to access the benchmark: https://multi-shot-edit.github.io/

Paper page on Hugging Face · arXiv

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