FlowAnchor：稳定无反转视频编辑中的编辑信号

摘要

我们提出FlowAnchor，一种无需训练即可实现稳定高效的无反演流式视频编辑框架。近年来，无反演编辑方法通过直接利用编辑信号引导采样轨迹，在图像领域展现出卓越的效率和结构保持能力。然而将该范式扩展至视频领域仍面临挑战，在多物体场景或增加帧数时往往失效。我们发现根本原因在于高维视频潜空间中编辑信号的不稳定性，这种不稳定性源于空间定位不精确和长度引发的幅度衰减。为攻克此难题，FlowAnchor通过双重锚定机制明确规范编辑位置与编辑强度：引入空间感知注意力优化机制，强制文本引导与空间区域保持一致性对齐；采用自适应幅度调制技术，动态维持足够的编辑强度。这两种机制协同作用，可稳定编辑信号并引导流式演化朝向目标分布。大量实验表明，FlowAnchor在多物体快速运动等复杂场景下，能实现更逼真、时序连贯且计算高效的视频编辑。项目页面详见https://cuc-mipg.github.io/FlowAnchor.github.io/。

English

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/.

FlowAnchor：稳定无反转视频编辑中的编辑信号

FlowAnchor: Stabilizing the Editing Signal for Inversion-Free Video Editing

摘要

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