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FlowAnchor addresses the instability of editing signals in flow-based video editing, which leads to artifacts in multi-object and long-duration videos. They introduce Spatial-aware Attention Refinement to improve alignment between text guidance and spatial regions, and Adaptive Magnitude Modulation to maintain editing strength. Experiments show FlowAnchor achieves more faithful, temporally coherent, and efficient video editing compared to existing methods.
FlowAnchor makes flow-based video editing robust to multi-object scenes and long sequences by stabilizing the editing signal, opening the door to more complex and controllable video manipulation.
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/.
