Yingshuang Zou, Xiaoyu Shi, Yawen Luo, Ziang Li, Qinghe Wang, Mengfei Li, Chengfeng Zhao, Xintao Wang, Pengfei Wan, Kun Gai, Yuan Liu · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26801 · cited by 0 works (OpenAlex) · open-access PDF
テキストからビデオの生成における最近の進歩は、高い信頼性のあるシングルショット合成を可能にしましたが、実践的なストーリー作成には、強力なインターショット一致性と繊細なコントロール性の両方を持つ複数のショットビデオが必要です。既存の複数のショットビデオ生成のアプローチは特に強力な一貫性がありますが、コントロール可能性はまだ低いです。この論文では、Mise-en-Video(MeV)は、ビデオクリエイターが3Dスペースで複数のショットをステージ化し、それに応じた複数のショットビデオを生成することを可能にする非常にコントロール可能な複数のショットビデオ生成フレームワークです。これを実現する
Machine translation (Meta M2M100); the English original follows.
Recent advances in text-to-video generation have enabled high-fidelity single-shot synthesis, yet practical storytelling creation requires multi-shot videos with both strong inter-shot consistency and fine-grained controllability. Existing multi-shot video generation approaches achieve strong consistency but still fall short in controllability. In this paper, we propose Mise-en-Video (MeV), a highly controllable multi-shot video generation framework that allows video creators to conduct staging the multi-shot in the 3D space and then generate the corresponding multi-shot video. To achieve this, our framework combines a hierarchical attention mechanism for multi-level narrative consistency with 3D-aware layout guidance for precise, per-shot spatial control. To support training and evaluation, we develop standardized annotation pipelines and construct a 160K-video dataset with hierarchical multi-shot texts and oriented 3D bounding boxes. Extensive experiments demonstrate that MeV consistently outperforms strong baselines in video quality, inter-shot consistency, and subject-level controllability.
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Topics: Computer science, Consistency (knowledge bases), Bounding overwatch, Annotation, Artificial intelligence, Construct (python library)
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