MSEditor：面向一致性多镜头视频编辑
MSEditor: Toward Consistent Multi-Shot Video Editing
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中文总结 AI 辅助
针对多镜头视频编辑的身份漂移与累积误差问题，提出首个专用框架MSEditor，通过监督适配器与跨镜头打包策略实现一致性编辑，在基准上性能优于现有方法。
中文摘要 AI 辅助
本文研究对多镜头视频序列进行一致、统一修改的问题，该任务极具挑战性，因为多镜头视频由不连续的时间片段组成，在视角、相机尺度和主体姿态上差异显著，会导致严重的身份漂移和累积误差传播。实现连贯编辑需要建立可靠的跨镜头语义感知，以在这些不连续边界间维持稳定的主体外观和视觉连续性。为解决此问题，我们提出MSEditor，这是首个专门用于一致性多镜头视频编辑的框架。为克服高质量多镜头训练数据的稀缺性，我们复用现有多视角视频数据集以提供鲁棒的跨镜头监督。在架构上，我们引入监督适配器，将跨镜头信息注入扩散骨干网络，使模型学习到身份一致的表示。此外，为有效缓解累积误差并确保长程时间一致性，我们设计跨镜头打包策略，在自注意力窗口内动态聚合语义相关镜头的信息。大量实验表明，在我们整理的多镜头视频编辑基准上，MSEditor在身份保留、时间稳定性和整体视觉质量方面显著优于现有方法。
英文摘要
In this paper, we tackle the problem of performing consistent, unified modifications to a multi-shot video sequence. This task is particularly challenging because multi-shot videos consist of discontinuous temporal segments that vary significantly in viewpoint, camera scale, and subject pose, leading to severe identity drift and cumulative error propagation. Achieving coherent edits requires establishing reliable cross-shot semantic awareness to maintain stable subject appearance and visual continuity across these disjointed boundaries. To address this, we propose MSEditor, the first framework designed specifically for consistent multi-shot video editing. To overcome the scarcity of high-quality multi-shot training data, we repurpose existing multi-view video datasets to provide robust cross-shot supervision. Architecturally, we introduce a Supervisory Adapter that injects this cross-shot information into the diffusion backbone, enabling the model to learn identity-consistent representations. Furthermore, to effectively mitigate cumulative errors and ensure long-range temporal coherence, we design a Cross-Shot Packing strategy that dynamically aggregates information from semantically related shots within the self-attention window. Extensive experiments demonstrate that MSEditor significantly outperforms existing methods on our curated multi-shot video editing benchmark in terms of identity preservation, temporal stability, and overall visual quality.
