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arxiv: 2604.22586 · v1 · submitted 2026-04-24 · 💻 cs.CV

Recognition: unknown

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

Ze Chen , Lan Chen , Yuanhang Li , Qi Mao
Authors on Pith no claims yet

Pith reviewed 2026-05-08 12:30 UTC · model grok-4.3

classification 💻 cs.CV
keywords video editinginversion-free editingflow-based editingattention refinementmagnitude modulationtemporal coherencediffusion modelstraining-free methods
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The pith

FlowAnchor stabilizes the editing signal for inversion-free video editing by explicitly anchoring both location and strength of changes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper identifies that inversion-free editing, which steers sampling directly with an editing signal, works well for images but breaks down in videos due to unstable signals in high-dimensional latent spaces. This instability stems from imprecise spatial localization of edits and weakening of the signal as the number of frames grows. FlowAnchor counters this with two mechanisms that enforce consistent text-to-region alignment and preserve editing magnitude throughout the process. If successful, it delivers more accurate, coherent edits in complex scenes without the cost of inversion steps. The result is practical for handling multiple moving objects or longer clips while keeping computation low.

Core claim

FlowAnchor is a training-free framework that stabilizes inversion-free, flow-based video editing by anchoring both where to edit and how strongly to edit. It introduces Spatial-aware Attention Refinement to enforce consistent alignment between textual guidance and spatial regions, and Adaptive Magnitude Modulation to adaptively preserve sufficient editing strength against attenuation. These mechanisms together stabilize the editing signal and guide the flow-based evolution toward the desired target distribution, enabling reliable performance in multi-object and fast-motion videos.

What carries the argument

Spatial-aware Attention Refinement and Adaptive Magnitude Modulation, which anchor the spatial location and magnitude of the editing signal in video latent spaces.

If this is right

  • Editing becomes feasible in multi-object scenes where previous methods lose track of individual elements.
  • Temporal coherence improves in fast-motion videos by maintaining consistent edit strength across frames.
  • Computation stays efficient since no inversion step is required even for longer sequences.
  • The flow-based sampling trajectory is steered more reliably toward the target distribution.
  • Performance scales better with increased frame counts without additional training.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same anchoring idea might stabilize editing signals in other high-dimensional generative tasks such as 3D or audio-conditioned video synthesis.
  • It points to a general principle that explicit spatial and magnitude control could reduce reliance on inversion across diffusion-based editing pipelines.
  • Future work could test whether combining the two mechanisms with existing attention variants yields further gains in structure preservation.

Load-bearing premise

The root cause of failure in extending inversion-free editing to videos is instability of the editing signal from imprecise spatial localization and length-induced magnitude attenuation.

What would settle it

A direct comparison showing that videos edited without the two anchoring mechanisms exhibit the same spatial drift and signal weakening failures as prior inversion-free baselines on multi-object or high-frame-count sequences.

Figures

Figures reproduced from arXiv: 2604.22586 by Lan Chen, Qi Mao, Yuanhang Li, Ze Chen.

Figure 1
Figure 1. Figure 1: FlowAnchor stabilizes inversion-free video editing across diverse challenging scenarios. While the inversion-free view at source ↗
Figure 2
Figure 2. Figure 2: Wan-Edit [17] vs. Ours. (a) Naively extending FlowEdit [14] to videos such as Wan-Edit [17] produces un￾stable editing signals, causing the editing trajectory to distort and resulting in suboptimal edits. (b) FlowAnchor provides an explicit anchor to stabilize the editing trajectory toward the intended target. modes: localization diffusion and length-induced magnitude attenuation. • We introduce FlowAnchor… view at source ↗
Figure 3
Figure 3. Figure 3: Challenges of unstable editing signals in existing inversion-free video editing. (a) In multi-object scenes, the editing view at source ↗
Figure 4
Figure 4. Figure 4: Framework of FlowAnchor. (a) At each timestep, view at source ↗
Figure 5
Figure 5. Figure 5: User preference study. We report the preference view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparisons with baselines. Our FlowAnchor outperforms baseline methods in both editing localization view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative analysis on SAR and AMM modules. view at source ↗
Figure 10
Figure 10. Figure 10: Robustness to mask granularity. FlowAnchor pro view at source ↗
Figure 11
Figure 11. Figure 11: Limitations in global style transfer and motion view at source ↗
Figure 12
Figure 12. Figure 12: Examples and annotations in Anchor-Bench. Anchor-Bench covers three localized editing types, including color view at source ↗
Figure 13
Figure 13. Figure 13: Ablation on hyperparameters of SAR and AMM. (a) Effect of SAR strengths view at source ↗
Figure 14
Figure 14. Figure 14: Effect of SAR application range. We vary the cutoff view at source ↗
Figure 15
Figure 15. Figure 15: Comparison with the inpainting-based method VACE [ view at source ↗
Figure 16
Figure 16. Figure 16: Comparison with the inpainting-based method VACE [ view at source ↗
Figure 17
Figure 17. Figure 17: Comparison with FlowDirector. FlowDirector derives an implicit spatial mask from the source and target CA maps view at source ↗
Figure 18
Figure 18. Figure 18: Qualitative results of FlowAnchor. FlowAnchor handles a wide range of editing tasks, including color editing, texture view at source ↗
Figure 19
Figure 19. Figure 19: Qualitative results of FlowAnchor. FlowAnchor handles a wide range of editing tasks, including color editing, texture view at source ↗
Figure 20
Figure 20. Figure 20: Qualitative results of FlowAnchor. FlowAnchor handles a wide range of editing tasks, including color editing, texture view at source ↗
Figure 21
Figure 21. Figure 21: Qualitative results of FlowAnchor. FlowAnchor handles a wide range of editing tasks, including color editing, texture view at source ↗
read the original abstract

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/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper proposes FlowAnchor, a training-free framework for inversion-free, flow-based video editing. It diagnoses failures when extending image-based inversion-free editing to video as arising from instability of the editing signal in high-dimensional video latent spaces, specifically due to imprecise spatial localization and length-induced magnitude attenuation. The method introduces two mechanisms—Spatial-aware Attention Refinement to enforce consistent alignment between textual guidance and spatial regions, and Adaptive Magnitude Modulation to adaptively preserve editing strength—to stabilize the signal and steer the flow-based sampling toward the target distribution. The authors report that these components yield more faithful, temporally coherent, and computationally efficient results than prior approaches, particularly in multi-object and fast-motion scenarios, as demonstrated by extensive experiments.

Significance. If the empirical results hold, the work is significant because it provides a practical, training-free solution to a known scalability bottleneck in video editing without relying on costly inversion steps. The explicit identification of spatial and magnitude instabilities as root causes, followed by targeted anchoring mechanisms, offers a clear and interpretable advance over generic flow-steering baselines. The training-free property and focus on flow-based evolution without added parameters are notable strengths that could improve reproducibility and deployment.

minor comments (3)
  1. [Abstract] Abstract: the claim of 'extensive experiments' demonstrating improvements would be strengthened by including at least one or two key quantitative metrics (e.g., CLIP similarity or temporal consistency scores) alongside the qualitative description.
  2. [Method] The manuscript should clarify in the method section whether the two proposed modules (SAR and AMM) interact in a way that could introduce unintended dependencies on video length or object count; a short derivation or pseudocode would help.
  3. [Experiments] Figure captions and legends should explicitly state the baselines used and the exact metrics plotted to allow readers to assess the claimed gains in faithfulness and coherence without cross-referencing the text.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of our manuscript and the recommendation for minor revision. We appreciate the acknowledgment of the significance of our training-free FlowAnchor framework, particularly its identification of spatial and magnitude instabilities in video latent spaces and the targeted mechanisms to address them.

Circularity Check

0 steps flagged

No significant circularity detected in the presented framework

full rationale

The paper articulates an externally motivated problem (instability of editing signals in video latents due to spatial imprecision and magnitude attenuation) and proposes two explicit, training-free mechanisms (Spatial-aware Attention Refinement and Adaptive Magnitude Modulation) to stabilize the flow-based editing trajectory. No equations, derivations, or self-citations appear in the abstract or description that reduce the central claim to a fitted parameter, self-definition, or load-bearing prior result from the same authors. The method is presented as a direct engineering response to an identified failure mode rather than a closed logical loop, rendering the derivation chain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are stated. The approach implicitly relies on standard assumptions of flow-based generative models for videos.

axioms (1)
  • domain assumption Flow-based generative models for video admit direct steering of sampling trajectories by editing signals
    The entire inversion-free paradigm rests on this property of the underlying generative model.

pith-pipeline@v0.9.0 · 5493 in / 1277 out tokens · 65290 ms · 2026-05-08T12:30:43.738958+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

39 extracted references · 8 canonical work pages · 2 internal anchors

  1. [1]

    Lingling Cai, Kang Zhao, Hangjie Yuan, Xiang Wang, Yingya Zhang, and Kejie Huang. 2025. DFVEdit: Conditional Delta Flow Vector for Zero-shot Video Editing.arXiv preprint arXiv:2506.20967(2025)

    work page arXiv 2025

  2. [2]

    Duygu Ceylan, Chun-Hao P Huang, and Niloy J Mitra. 2023. Pix2video: Video edit- ing using image diffusion. InProceedings of the IEEE/CVF international conference on computer vision. 23206–23217

    2023

  3. [3]

    Yuren Cong, Mengmeng Xu, Shoufa Chen, Jiawei Ren, Yanping Xie, Juan-Manuel Perez-Rua, Bodo Rosenhahn, Tao Xiang, Sen He, et al. 2024. FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing. InThe Twelfth International Conference on Learning Representations

    2024

  4. [4]

    Michal Geyer, Omer Bar-Tal, Shai Bagon, and Tali Dekel. 2024. TokenFlow: Consis- tent Diffusion Features for Consistent Video Editing. InThe Twelfth International Conference on Learning Representations

    2024

  5. [5]

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. 2022. Video diffusion models.Advances in neu- ral information processing systems35 (2022), 8633–8646

    2022

  6. [6]

    Quan Huynh-Thu and Mohammed Ghanbari. 2008. Scope of validity of PSNR in image/video quality assessment.Electronics letters44, 13 (2008), 800–801

    2008

  7. [7]

    Zeyinzi Jiang, Zhen Han, Chaojie Mao, Jingfeng Zhang, Yulin Pan, and Yu Liu

  8. [8]

    InProceedings of the IEEE/CVF International Conference on Computer Vision

    Vace: All-in-one video creation and editing. InProceedings of the IEEE/CVF International Conference on Computer Vision. 17191–17202

  9. [9]

    Guanlong Jiao, Biqing Huang, Kuan-Chieh Wang, and Renjie Liao. 2025. Uniedit- flow: Unleashing inversion and editing in the era of flow models.arXiv preprint arXiv:2504.13109(2025)

    work page arXiv 2025

  10. [10]

    Ozgur Kara, Bariscan Kurtkaya, Hidir Yesiltepe, James M Rehg, and Pinar Ya- nardag. 2024. Rave: Randomized noise shuffling for fast and consistent video editing with diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6507–6516

    2024

  11. [11]

    Jeongsol Kim, Yeobin Hong, Jonghyun Park, and Jong Chul Ye. 2025. Flowalign: Trajectory-regularized, inversion-free flow-based image editing.arXiv preprint arXiv:2505.23145(2025)

    work page arXiv 2025

  12. [12]

    Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al

  13. [13]

    InProceedings of the IEEE/CVF international conference on computer vision

    Segment anything. InProceedings of the IEEE/CVF international conference on computer vision. 4015–4026

  14. [14]

    Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. 2024. Hunyuanvideo: A systematic framework for large video generative models.arXiv preprint arXiv:2412.03603 (2024)

    work page internal anchor Pith review arXiv 2024

  15. [15]

    Xianghao Kong, Hansheng Chen, Yuwei Guo, Lvmin Zhang, Gordon Wetzstein, Maneesh Agrawala, and Anyi Rao. 2025. Taming flow-based i2v models for creative video editing.arXiv preprint arXiv:2509.21917(2025)

    work page arXiv 2025

  16. [16]

    Vladimir Kulikov, Matan Kleiner, Inbar Huberman-Spiegelglas, and Tomer Michaeli. 2025. Flowedit: Inversion-free text-based editing using pre-trained flow models. InProceedings of the IEEE/CVF International Conference on Computer Vision. 19721–19730

    2025

  17. [17]

    Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang, Eli Shechtman, Ersin Yumer, and Ming-Hsuan Yang. 2018. Learning blind video temporal consistency. InProceed- ings of the European conference on computer vision (ECCV). 170–185

    2018

  18. [18]

    Guangzhao Li, Yanming Yang, Chenxi Song, and Chi Zhang. 2025. Flowdirector: Training-free flow steering for precise text-to-video editing.arXiv preprint arXiv:2506.05046(2025)

    work page arXiv 2025

  19. [19]

    Minghan Li, Chenxi Xie, Yichen Wu, Lei Zhang, and Mengyu Wang. 2025. Five- bench: A fine-grained video editing benchmark for evaluating emerging diffusion and rectified flow models. InProceedings of the IEEE/CVF International Conference on Computer Vision. 16672–16681

    2025

  20. [20]

    Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. 2023. Flow Matching for Generative Modeling. InThe Eleventh International Conference on Learning Representations

    2023

  21. [21]

    Xingchao Liu, Chengyue Gong, et al. 2023. Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. InThe Eleventh International Conference on Learning Representations

    2023

  22. [22]

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El- Nouby, et al. 2024. DINOv2: Learning Robust Visual Features without Supervision. Transactions on Machine Learning Research Journal(2024)

    2024

  23. [23]

    William Peebles and Saining Xie. 2023. Scalable diffusion models with transform- ers. InProceedings of the IEEE/CVF international conference on computer vision. 4195–4205

    2023

  24. [24]

    Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, and Alexander Sorkine-Hornung. 2016. A benchmark dataset and eval- uation methodology for video object segmentation. InProceedings of the IEEE conference on computer vision and pattern recognition. 724–732

    2016

  25. [25]

    Chenyang Qi, Xiaodong Cun, Yong Zhang, Chenyang Lei, Xintao Wang, Ying Shan, and Qifeng Chen. 2023. Fatezero: Fusing attentions for zero-shot text-based video editing. InProceedings of the IEEE/CVF International Conference on Computer Vision. 15932–15942

    2023

  26. [26]

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. InInternational conference on machine learning. PmLR, 8748–8763

    2021

  27. [27]

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10684–10695

    2022

  28. [28]

    Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. 2023. Make-A-Video: Text-to-Video Generation without Text-Video Data. InThe Eleventh International Conference on Learning Representations

    2023

  29. [29]

    Zachary Teed and Jia Deng. 2020. Raft: Recurrent all-pairs field transforms for optical flow. InEuropean conference on computer vision. Springer, 402–419

    2020

  30. [30]

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. 2025. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314(2025)

    work page internal anchor Pith review arXiv 2025

  31. [31]

    Jiangshan Wang, Junfu Pu, Zhongang Qi, Jiayi Guo, Yue Ma, Nisha Huang, Yuxin Chen, Xiu Li, and Ying Shan. 2025. Taming Rectified Flow for Inversion and Editing. InInternational Conference on Machine Learning. PMLR, 64044–64058

    2025

  32. [32]

    Yukun Wang, Longguang Wang, Zhiyuan Ma, Qibin Hu, Kai Xu, and Yulan Guo

  33. [33]

    InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Videodirector: Precise video editing via text-to-video models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2589– 2598

  34. [34]

    Sihan Xu, Yidong Huang, Jiayi Pan, Ziqiao Ma, and Joyce Chai. 2024. Inversion- free image editing with language-guided diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9452–9461

    2024

  35. [35]

    Shuai Yang, Yifan Zhou, Ziwei Liu, and Chen Change Loy. 2023. Rerender a video: Zero-shot text-guided video-to-video translation. InSIGGRAPH Asia 2023 Conference Papers. 1–11

    2023

  36. [36]

    Xiangpeng Yang, Linchao Zhu, Hehe Fan, and Yi Yang. 2025. Videograin: Mod- ulating space-time attention for multi-grained video editing. InThe Thirteenth International Conference on Learning Representations

    2025

  37. [37]

    Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al . 2025. CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer. InThe Thirteenth International Conference on Learning Representations

    2025

  38. [38]

    Sung-Hoon Yoon, Minghan Li, Gaspard Beaudouin, Congcong Wen, Muham- mad Rafay Azhar, and Mengyu Wang. 2025. SplitFlow: Flow Decomposition for Inversion-Free Text-to-Image Editing. InThe Thirty-ninth Annual Conference on Neural Information Processing Systems

    2025

  39. [39]

    Controlvideo: Training-free controllable text-to-video generation

    Yabo Zhang, Yuxiang Wei, Dongsheng Jiang, Xiaopeng Zhang, Wangmeng Zuo, and Qi Tian. 2023. ControlVideo: Training-free Controllable Text-to-Video Gen- eration.arXiv preprint arXiv:2305.13077(2023). Under Review, Chen et al. A Summary In this supplementary material, we provide additional technical details, benchmark descriptions, and qualitative analyses. ...

    work page arXiv 2023