ASTRA: 让任意主体在视频编辑中变换
ASTRA: Let Arbitrary Subjects Transform in Video Editing
AI总结：
ASTRA提出一种无需训练的视频编辑框架，通过多模态对齐模块和先验掩码重定位模块解决密集多主体场景中的注意力稀释和边界纠缠问题，实现精准操控与非目标区域的严格保留。
摘要：
尽管现有视频编辑方法在单主体场景中表现优异，但在密集多主体场景中常面临注意力稀释和掩码边界纠缠问题，导致属性泄漏和时间不稳定性。为此，我们提出了ASTRA，一种无需模型微调的无缝任意主体视频编辑框架。ASTRA通过提示引导的多模态对齐模块生成稳健的条件以缓解注意力稀释，并通过基于先验的掩码重定位模块生成时间一致的掩码序列以解决边界纠缠问题。ASTRA作为多功能的即插即用模块，可无缝集成到各种基于掩码的视频生成器中。在我们新构建的基准MSVBench上的广泛实验表明，ASTRA在一致性方面优于最先进的方法。代码、模型和数据可在https://github.com/XWH-A/ASTRA获取。
英文摘要：
While existing video editing methods excel with single subjects, they struggle in dense, multi-subject scenes, frequently suffering from attention dilution and mask boundary entanglement that cause attribute leakage and temporal instability. To address this, we propose ASTRA, a training-free framework for seamless, arbitrary-subject video editing. Without requiring model fine-tuning, ASTRA precisely manipulates multiple designated subjects while strictly preserving non-target regions. It achieves this via two core components: a prompt-guided multimodal alignment module that generates robust conditions to mitigate attention dilution, and a prior-based mask retargeting module that produces temporally coherent mask sequences to resolve boundary entanglement. Functioning as a versatile plug-and-play module, ASTRA seamlessly integrates with diverse mask-driven video generators. Extensive experiments on our newly constructed benchmark, MSVBench, demonstrate that ASTRA consistently outperforms state-of-the-art methods. Code, models, and data are available at https://github.com/XWH-A/ASTRA.
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。
