DreamSwapV：用于任意定制视频编辑的掩码引导主体替换
DreamSwapV: Mask-guided Subject Swapping for Any Customized Video Editing
AI总结：
针对定制视频编辑中主体替换研究不足的问题，提出掩码引导、主体无关的端到端框架DreamSwapV，通过多条件融合模块和自适应掩码策略实现任意主体替换，在VBench及新基准上性能优于现有方法。
摘要：
随着视频生成技术的快速发展，定制视频编辑的需求激增，其中主体替换是关键组成部分，但相关研究仍不够充分。主流替换方法要么专注于狭窄领域（如人体动画或手-物交互），要么依赖间接编辑范式或模糊文本提示，影响最终保真度。本文提出DreamSwapV，这是一个掩码引导、主体无关的端到端框架，可利用用户指定的掩码和参考图像，在任意视频中替换任意主体以实现定制化。为注入细粒度引导，我们引入多种条件及专用条件融合模块，高效整合这些条件。此外，设计自适应掩码策略以适应不同尺度和属性的主体，进一步改善替换主体与周围环境的交互。通过精心设计的两阶段数据集构建和训练方案，DreamSwapV在VBench指标及我们首次引入的DreamSwapV-Benchmark上的综合实验验证中，性能优于现有方法。
英文摘要：
With the rapid progress of video generation, demand for customized video editing is surging, where subject swapping constitutes a key component yet remains under-explored. Prevailing swapping approaches either specialize in narrow domains--such as human-body animation or hand-object interaction--or rely on some indirect editing paradigm or ambiguous text prompts that compromise final fidelity. In this paper, we propose DreamSwapV, a mask-guided, subject-agnostic, end-to-end framework that swaps any subject in any video for customization with a user-specified mask and reference image. To inject fine-grained guidance, we introduce multiple conditions and a dedicated condition fusion module that integrates them efficiently. In addition, an adaptive mask strategy is designed to accommodate subjects of varying scales and attributes, further improving interactions between the swapped subject and its surrounding context. Through our elaborate two-phase dataset construction and training scheme, our DreamSwapV outperforms existing methods, as validated by comprehensive experiments on VBench indicators and our first introduced DreamSwapV-Benchmark.
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。
