IDAG-Edit: 基于实例解耦注意力和引导的多对象视频编辑
IDAG-Edit: Multi-Object Video Editing via Instance-Decoupled Attention and Guidance
AI总结：
提出IDAG-Edit框架，通过布局引导注意力调制和实例级掩码实现无训练的多对象视频编辑，解决注意力泄露和身份漂移问题，提升时间一致性和多对象可控性。
摘要：
基于扩散的视频编辑已取得显著进展；然而，在多对象场景中，由于注意力泄露、身份漂移和不稳定的时间动态，实现精确且时间一致的对象级控制仍然具有挑战性。在这项工作中，我们提出了IDAG-Edit，一个无需训练的框架，用于具有强时间一致性的细粒度多对象视频编辑。该框架采用布局引导的注意力调制来促进连贯的多对象编辑，同时引入实例级掩码以保留单个对象身份并在每个对象区域内强制执行局部注意力，从而实现细粒度的对象级编辑。广泛的定性和定量评估表明，我们的方法在时间稳定性和多对象可控性方面优于最先进的视频编辑方法。
英文摘要：
Diffusion-based video editing has made significant progress; however, achieving precise and temporally consistent object-level control, especially in multi-object scenarios, remains challenging due to attention leakage, identity drift, and unstable temporal dynamics. In this work, we propose IDAGEdit, a training-free framework for fine-grained multi-object video editing with strong temporal consistency. The framework adopts Layout-guided Attention Modulation to facilitate coherent multi-object editing, while Instance-level Masks are introduced to preserve individual object identity and enforce localized attention within each object region, thereby enabling fine-grained, object-level editing. Extensive qualitative and quantitative evaluations demonstrate that our method improves temporal stability and multi-object controllability over state-of-the-art video editing approaches.
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。
