FlowAnchor：稳定编辑信号以实现无反向视频编辑
FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing
AI总结：
本文提出FlowAnchor框架，通过稳定编辑信号实现高效的无反向视频编辑，解决多目标场景和高速运动中的稳定性问题。
摘要：
我们提出FlowAnchor，一种无需训练的框架，用于稳定且高效的无反向流基视频编辑。无反向编辑方法最近在图像中展示了出色的效率和结构保持，通过直接引导采样轨迹的编辑信号实现。然而，将此范式扩展到视频仍具挑战性，通常在多目标场景或增加帧数时失效。我们发现根本原因是高维视频潜在空间中编辑信号的不稳定，这源于空间定位不精确和长度导致的幅度衰减。为克服这一挑战，FlowAnchor明确锚定编辑位置和编辑强度。它引入了空间感知注意力细化，强制文本指导与空间区域之间的一致对齐，并引入自适应幅度调制，自适应地保持足够的编辑强度。这些机制共同稳定了编辑信号，并引导基于流的进化朝着目标分布发展。大量实验表明，FlowAnchor在挑战性的多目标和高速运动场景中实现了更忠实、时间一致且计算高效的视频编辑。项目页面可在https://cuc-mipg.github.io/FlowAnchor.github.io/上找到。
英文摘要：
We propose FlowAnchor, a training-free framework for stable and efficient inversion-free, flow-based video editing. Inversion-free editing methods have recently shown impressive efficiency and structure preservation in images by directly steering the sampling trajectory with an editing signal. However, extending this paradigm to videos remains challenging, often failing in multi-object scenes or with increased frame counts. We identify the root cause as the instability of the editing signal in high-dimensional video latent spaces, which arises from imprecise spatial localization and length-induced magnitude attenuation. To overcome this challenge, FlowAnchor explicitly anchors both where to edit and how strongly to edit. It introduces Spatial-aware Attention Refinement, which enforces consistent alignment between textual guidance and spatial regions, and Adaptive Magnitude Modulation, which adaptively preserves sufficient editing strength. Together, these mechanisms stabilize the editing signal and guide the flow-based evolution toward the desired target distribution. Extensive experiments demonstrate that FlowAnchor achieves more faithful, temporally coherent, and computationally efficient video editing across challenging multi-object and fast-motion scenarios. The project page is available at https://cuc-mipg.github.io/FlowAnchor.github.io/.
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。
