FlowPortal：残差校正流用于免训练视频重打光与背景替换
FlowPortal: Residual-Corrected Flow for Training-Free Video Relighting and Background Replacement
把标准 flow-based 生成模型通过残差校正改造成免训练编辑模型：条件相同时保证完美重建、条件不同时实现忠实编辑，这一思路可直接迁移到视频/图像的其他属性编辑（重打光、换背景、风格化）而无需微调。
原文依据 a Residual-Corrected Flow mechanism that transforms a standard flow-based model into an editing model
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视频重打光与背景替换是一项具有挑战性的任务，对电影制作和创意媒体应用至关重要。现有方法难以在时序一致性、空间保真度和光照自然度之间取得平衡。为解决这些问题，我们提出 FlowPortal，一种新颖的免训练、基于流的视频重打光框架。我们的核心创新是残差校正流机制，它将标准的基于流的模型转变为编辑模型：当输入条件相同时保证完美重建，当条件不同时实现忠实的重打光，从而获得较高的结构一致性。该机制进一步由解耦条件设计增强，以实现精确的光照控制，并由高频迁移机制增强，以保持细节。此外，一种掩码策略将前景重打光与背景纯生成过程隔离开来。实验表明，FlowPortal 在时序连贯性、结构保持和光照真实感方面取得了更优的性能，同时保持高效率。
英文原文摘要 1071 字符
Video relighting with background replacement is a challenging task critical for applications in film production and creative media. Existing methods struggle to balance temporal consistency, spatial fidelity, and illumination naturalness. To address these issues, we introduce FlowPortal, a novel training-free flow-based video relighting framework. Our core innovation is a Residual-Corrected Flow mechanism that transforms a standard flow-based model into an editing model, guaranteeing perfect reconstruction when input conditions are identical and enabling faithful relighting when they differ, resulting in high structural consistency. This is further enhanced by a Decoupled Condition Design for precise lighting control and a High-Frequency Transfer mechanism for detail preservation. Additionally, a masking strategy isolates foreground relighting from background pure generation process. Experiments demonstrate that FlowPortal achieves superior performance in temporal coherence, structural preservation, and lighting realism, while maintaining high efficiency.