MoviEdit: Long-form Multi-Shot Video Editing Made Easy
MoviEdit: Long-form Multi-Shot Video Editing Made Easy
MoviEdit framework, through cascaded bidirectional autoregressive modeling and new benchmark tests, maintains cross-scene coherence while reducing manual data annotation, significantly outperforming existing open-source models with performance comparable to industry leading models. The framework automatically generates 87K pairs of paired editing videos for cross-shot supervision, establishes a global editing draft using a lightweight bidirectional stage, and then performs autoregressive propagation and refinement.
Abstract
MoviEdit is an end-to-end framework for long-form multi-shot video editing that uses cascaded bidirectional-autoregressive modeling and a new benchmark to maintain coherence across scenes while reducing manual data annotation.
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/
View arXiv page View PDF Project page GitHub 2 Add to collection
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2611.02737 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2611.02737 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2611.02737 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.
Source: Hugging Face Daily Papers · huggingface.co