MoviEdit：长片段多镜头视频编辑框架
MoviEdit: Long-form Multi-Shot Video Editing Made Easy
MoviEdit 框架通过级联双向自回归建模和新的基准测试，在减少人工数据标注的同时保持跨场景的连贯性，大幅优于现有开源模型，性能与行业前沿模型相当。该框架自动生成 87K 对配对编辑视频用于跨镜头监督，采用轻量级双向阶段建立全局编辑草稿，再进行自回归传播和细化。
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/
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来源: Hugging Face Daily Papers · huggingface.co