Mise-en-Video：多镜头视频生成的细粒度 3D 控制框架
Mise-en-Video: Fine-Grained 3D Control for Multi-Shot Video Generation
做视频生成的朋友可以看看这篇：先在 3D 空间里摆好镜头再生成多镜头视频，可控性比现有方案强不少。
论文提出 Mise-en-Video（MeV），一个多镜头视频生成框架，创作者可先在 3D 空间中编排各镜头，再生成对应视频。框架采用分层注意力机制维持多级叙事一致性，并用 3D 感知的布局引导实现每个镜头的精确空间控制。团队构建了包含 16 万条视频的数据集，带有分层多镜头文本和带朝向的 3D 边界框标注，用于训练与评估。实验显示 MeV 在视频质量、镜头间一致性和主体级可控性上均优于基线方法。
Mise-en-Video: Fine-Grained 3D Control for Multi-Shot Video Generation
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.
