MuSS：一个多镜头数据集和电影叙事基准用于多镜头主体到视频生成
MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation
AI总结：
本文提出MuSS数据集和电影叙事基准，解决多镜头视频生成中的叙事逻辑、时空对齐和复制粘贴问题，通过改进的标注流程和反复制粘贴指标提升生成质量。
摘要：
尽管视频基础模型在单镜头生成上表现优异，但现实中的电影叙事本质上依赖复杂的多镜头序列。进一步进展受限于缺乏解决三个核心挑战的数据集：真实叙事逻辑、时空文本-视频对齐冲突以及主体到视频（S2V）生成中的“复制粘贴”难题。为此，我们引入MuSS，一个大规模双轨数据集，专门用于多镜头视频和S2V生成。该数据集源自超过3000部电影，支持复杂的蒙太奇过渡和以主体为中心的叙事。为构建该数据集，我们首创了一种渐进式标注流程，通过确保局部镜头级准确性后再强制全球叙事一致性来消除上下文冲突。关键在于我们实现了跨镜头匹配机制，从根本上消除S2V中的复制粘贴捷径。此外，我们提出了电影叙事基准，包含视觉逻辑驱动范式和新颖的反复制粘贴方差（ACP-Var）度量，以严格评估连续叙事和3D结构一致性。大量实验表明，尽管当前基线在连续叙事逻辑或退化为2D贴纸生成器方面挣扎，但我们的MuSS增强模型在叙事效果和跨镜头身份保持方面达到了最先进的水平。
英文摘要：
While video foundation models excel at single-shot generation, real-world cinematic storytelling inherently relies on complex multi-shot sequencing. Further progress is constrained by the absence of datasets that address three core challenges: authentic narrative logic, spatiotemporal text-video alignment conflicts, and the "copy-paste" dilemma prevalent in Subject-to-Video (S2V) generation. To bridge this gap, we introduce MuSS, a large-scale, dual-track dataset tailored for multi-shot video and S2V generation. Sourced from over 3,000 movies, MuSS explicitly supports both complex montage transitions and subject-centric narratives. To construct this dataset, we pioneer a progressive captioning pipeline that eliminates contextual conflicts by ensuring local shot-level accuracy before enforcing global narrative coherence. Crucially, we implement a cross-shot matching mechanism to fundamentally eradicate the S2V copy-paste shortcut. Alongside the dataset, we propose the Cinematic Narrative Benchmark, featuring a visual-logic-driven paradigm and a novel Anti-Copy-Paste Variance (ACP-Var) metric to rigorously assess continuous storytelling and 3D structural consistency. Extensive experiments demonstrate that while current baselines struggle with continuous narrative logic or degenerate into trivial 2D sticker generators, our MuSS-augmented model achieves state-of-the-art narrative effectiveness and cross-shot identity preservation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
