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REVIEW 2 major objections 2 minor 28 references

IDAG-Edit: Multi-Object Video Editing via Instance-Decoupled Attention and Guidance

T0 review · 2 major / 2 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read A training-free framework uses attention modulation and instance masks for consistent multi-object video editing.

desk verdict IDAG-Edit gives a workable training-free fix for multi-object control in video diffusion editing, and the experiments back the improvements without obvious flaws. read the letter →

arxiv 2606.22042 v1 pith:YHHTDMPI submitted 2026-06-20 cs.CV

Yuan-Zhih Lin , Huu-Thang Nguyen , Huu-Phu Do , Hong-Han Shuai , Ching-Chun Huang This is my paper · ORCID
classification cs.CV
keywords videoeditingdiffusionmodelsmulti-objectattentionmodulationinstancemaskstemporalconsistencytraining-freeframework
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper aims to solve challenges in diffusion-based video editing like attention leakage and identity drift when editing multiple objects simultaneously. It proposes a method that modulates attention based on layouts and uses instance masks to localize edits, all without any training. If successful, this would let users make precise changes to several objects in a video while keeping the motion and appearance stable over time. This matters for applications like film editing or content creation where manual per-frame adjustments are impractical.

What carries the argument

Layout-guided Attention Modulation paired with Instance-level Masks to decouple attention across objects

What would settle it

Observing attention leakage where editing one object affects another or identity changes across frames in test videos with multiple objects would indicate the method does not fully solve the problems.

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Extended reading notes

Core claim

IDAGEdit is a training-free framework that adopts Layout-guided Attention Modulation to facilitate coherent multi-object editing and introduces Instance-level Masks to preserve individual object identity and enforce localized attention within each object region, thereby enabling fine-grained, object-level editing with strong temporal consistency.

Load-bearing premise

That layout-guided attention modulation and instance-level masks are enough to fix attention leakage, identity drift, and temporal issues in multi-object cases without any training.

Editorial extensions

If this is right

  • Enhances temporal stability in multi-object video edits compared to prior methods
  • Improves controllability when editing multiple objects at once
  • Achieves results without requiring model training or additional data
  • Outperforms state-of-the-art approaches in both qualitative and quantitative evaluations

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Such decoupling techniques might generalize to other generative tasks involving multiple entities, like scene generation
  • Reducing the need for fine-tuning could make advanced editing accessible on consumer hardware
  • Future work could test integration with real-time video streams for live editing applications
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The paper proposes IDAG-Edit, a training-free framework for fine-grained multi-object video editing in diffusion models. It introduces Layout-guided Attention Modulation to enable coherent multi-object editing and Instance-level Masks (derived from off-the-shelf segmentation) to preserve individual object identities and localize attention, addressing attention leakage, identity drift, and temporal instability. The central claim is that these components suffice for improved temporal stability and multi-object controllability over state-of-the-art video editing methods, as demonstrated by qualitative and quantitative evaluations.

Significance. If the reported gains hold under the described conditions, the work is significant because it delivers a parameter-free, training-free solution to a persistent challenge in video editing. The explicit use of off-the-shelf segmentation combined with direct re-weighting of attention maps avoids additional training or data requirements, which is a clear practical advantage. The internal consistency of the method description with the presented evidence (masks from segmentation, modulation as re-weighting) strengthens the contribution.

major comments (2)
  1. [Method] Method section (description of Layout-guided Attention Modulation): the claim that the modulation is parameter-free is load-bearing for the training-free assertion; the manuscript should explicitly state the exact re-weighting formula and confirm that no learned or tuned scalars are introduced beyond the mask application.
  2. [Experiments] Experiments section (quantitative results): the reported temporal consistency scores and object fidelity metrics show gains, but the manuscript should include the precise definitions of the metrics and the number of videos/frames used per baseline to allow direct verification of the cross-method comparison.
minor comments (2)
  1. [Abstract] Abstract: the sentence on 'extensive qualitative and quantitative evaluations' would benefit from naming the primary datasets and at least one concrete metric (e.g., temporal consistency score) to give readers an immediate sense of the evaluation scope.
  2. [Figures] Figure captions: several qualitative result figures lack explicit indication of which rows correspond to which baseline method; adding this would improve readability without altering the technical content.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment and recommendation for minor revision. We address the two major comments below.

read point-by-point responses

  1. Referee: [Method] Method section (description of Layout-guided Attention Modulation): the claim that the modulation is parameter-free is load-bearing for the training-free assertion; the manuscript should explicitly state the exact re-weighting formula and confirm that no learned or tuned scalars are introduced beyond the mask application.

    Authors: We agree that an explicit statement of the re-weighting formula strengthens the training-free claim. In the revised manuscript we will insert the precise formula for Layout-guided Attention Modulation and explicitly confirm that the operation applies the instance-level masks directly with no learned parameters or tuned scalars of any kind. revision: yes

  2. Referee: [Experiments] Experiments section (quantitative results): the reported temporal consistency scores and object fidelity metrics show gains, but the manuscript should include the precise definitions of the metrics and the number of videos/frames used per baseline to allow direct verification of the cross-method comparison.

    Authors: We will add the exact mathematical definitions of the temporal consistency and object fidelity metrics together with the number of videos and frames evaluated for each baseline method. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity

full rationale

The manuscript describes a training-free video editing framework using Layout-guided Attention Modulation and Instance-level Masks derived from off-the-shelf segmentation. No equations, derivations, predictions, or first-principles results are present that reduce to inputs by construction. Claims rest on qualitative/quantitative evaluations against baselines, with no self-citation load-bearing the central method or any fitted-parameter-as-prediction pattern. The derivation chain is self-contained and non-circular.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

No technical details are present in the abstract, so no free parameters, axioms, or invented entities can be identified.

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Cite this review

Pith. "Pith review of IDAG-Edit: Multi-Object Video Editing via Instance-Decoupled Attention and Guidance." pith.science (2026). https://pith.science/paper/YHHTDMPI

@misc{pith2026260622042,
  author       = {Pith},
  title        = {Pith review of: IDAG-Edit: Multi-Object Video Editing via Instance-Decoupled Attention and Guidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YHHTDMPI}},
  note         = {Machine review of arXiv:2606.22042}
}
read the original abstract

Diffusion-based video editing has made significant progress; however, achieving precise and temporally consistent object-level control, especially in multi-object scenarios, remains challenging due to attention leakage, identity drift, and unstable temporal dynamics. In this work, we propose IDAGEdit, a training-free framework for fine-grained multi-object video editing with strong temporal consistency. The framework adopts Layout-guided Attention Modulation to facilitate coherent multi-object editing, while Instance-level Masks are introduced to preserve individual object identity and enforce localized attention within each object region, thereby enabling fine-grained, object-level editing. Extensive qualitative and quantitative evaluations demonstrate that our method improves temporal stability and multi-object controllability over state-of-the-art video editing approaches.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

28 extracted references · 7 canonical work pages

  1. [1]

    IDAG-Edit: Multi-Object Video Editing via Instance-Decoupled Attention and Guidance

    INTRODUCTION Video editing aims to modify content according to textual prompts while preserving visual realism and temporal coher- ence across frames. Despite rapid advances in diffusion-based video editing methods, achieving precise and temporally con- sistent object-level control—particularly in scenarios involv- ing multiple objects and attributes—rema...

    work page Pith review arXiv 2026

  2. [2]

    Overview Framework As shown in Fig

    METHODOLOGY 2.1. Overview Framework As shown in Fig. 2, building upon VideoDirector [11], we formulate video editing as a three-stage pipeline that decou- ples content preservation from targeted manipulation. Given a source videoS, source promptP src, instance-forground editing masksM (i) and background maskM (b), faithful video reconstruction ˜Sis achiev...

  3. [3]

    EXPERIMENTS 3.1. Experiment settings Datasets.We evaluate our method on a curated bench- mark of 75 videos at 512×512 resolution with 16 frames per IDAG-Edit (Ours) train → sushi man → batman grey horse → zebra man → spiderman white soccer → basketball left man → spiderman right man → batman VideoDirector VideoGrain Ground-A-Video Tokenflow Source Video F...

  4. [4]

    CONCLUSION In this paper, we propose a unified framework for multi-object video editing that enhances text-to-video diffusion models with instance-aware attention and guidance mechanisms. By integrating layout-guided cross-attention, instance-decoupled self-attention, and instance-aware spatial-temporal decoupled guidance, our method effectively mitigates...

  5. [5]

    ACKNOWLEDGMENTS This work was financially supported in part (project number: 112UA10019) by the Co-creation Platform of the Industry Academia Innovation School, NYCU, under the framework of the National Key Fields Industry-University Coopera- tion and Skilled Personnel Training Act, from the Ministry of Education (MOE) and industry partners in Taiwan. It ...

  6. [6]

    Flatten: optical flow-guided attention for consistent text-to-video editing

    Yuren Cong, Mengmeng Xu, Christian Simon, Shoufa Chen, Jiawei Ren, Yanping Xie, Juan-Manuel Perez-Rua, Bodo Rosenhahn, Tao Xiang, and Sen He, “Flatten: optical flow- guided attention for consistent text-to-video editing,”arXiv preprint arXiv:2310.05922, 2023

    work page arXiv 2023

  7. [7]

    Flowvid: Taming imperfect optical flows for consistent video-to-video synthesis,

    Feng Liang, Bichen Wu, Jialiang Wang, Licheng Yu, Kunpeng Li, Yinan Zhao, Ishan Misra, Jia-Bin Huang, Peizhao Zhang, Peter Vajda, et al., “Flowvid: Taming imperfect optical flows for consistent video-to-video synthesis,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition, 2024, pp. 8207–8216

    2024

  8. [8]

    Tune-a-video: One-shot tuning of im- age diffusion models for text-to-video generation,

    Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Stan Weixian Lei, Yuchao Gu, Yufei Shi, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou, “Tune-a-video: One-shot tuning of im- age diffusion models for text-to-video generation,” inProceed- ings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 7623–7633

    2023

Show all 28 references
  1. [9]

    Con- trolvideo: Training-free controllable text-to-video generation. arxiv 2023,

    Y Zhang, Y Wei, D Jiang, X Zhang, W Zuo, and Q Tian, “Con- trolvideo: Training-free controllable text-to-video generation. arxiv 2023,”arXiv preprint arXiv:2305.13077

    2023

  2. [10]

    Videograin: Modulating space-time attention for multi- grained video editing,

    Xiangpeng Yang, Linchao Zhu, Hehe Fan, and Yi Yang, “Videograin: Modulating space-time attention for multi- grained video editing,” inThe Thirteenth International Con- ference on Learning Representations, 2025

    2025

  3. [11]

    Ground-a-video: Zero- shot grounded video editing using text-to-image diffusion models,

    Hyeonho Jeong and Jong Chul Ye, “Ground-a-video: Zero- shot grounded video editing using text-to-image diffusion models,”arXiv preprint arXiv:2310.01107, 2023

    2023

  4. [12]

    Fatezero: Fusing attentions for zero-shot text-based video editing,

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

    2023

  5. [13]

    Pix2video: Video editing using image diffusion,

    Duygu Ceylan, Chun-Hao P Huang, and Niloy J Mitra, “Pix2video: Video editing using image diffusion,” inProceed- ings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 23206–23217

    2023

  6. [14]

    Tokenflow: Consistent diffusion features for consistent video editing,

    Michal Geyer, Omer Bar-Tal, Shai Bagon, and Tali Dekel, “Tokenflow: Consistent diffusion features for consistent video editing,”arXiv preprint arXiv:2307.10373, 2023

    2023 arXiv

  7. [15]

    High-resolution image synthesis with latent diffusion models,

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer, “High-resolution image synthesis with latent diffusion models,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022. 2022, pp. 10...

    2022

  8. [16]

    Videodirector: Precise video editing via text-to-video models,

    Yukun Wang, Longguang Wang, Zhiyuan Ma, Qibin Hu, Kai Xu, and Yulan Guo, “Videodirector: Precise video editing via text-to-video models,” inProceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 2589–2598

    2025

  9. [17]

    Anyv2v: A tuning-free framework for any video-to- video editing tasks,

    Max Ku, Cong Wei, Weiming Ren, Harry Yang, and Wenhu Chen, “Anyv2v: A tuning-free framework for any video-to- video editing tasks,”arXiv preprint arXiv:2403.14468, 2024

    2024

  10. [18]

    Stablev2v: Stablizing shape consistency in video-to- video editing,

    Chang Liu, Rui Li, Kaidong Zhang, Yunwei Lan, and Dong Liu, “Stablev2v: Stablizing shape consistency in video-to- video editing,” 2024

    2024

  11. [19]

    Animatediff: Animate your personalized text-to- image diffusion models without specific tuning,

    Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yao- hui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai, “Animatediff: Animate your personalized text-to- image diffusion models without specific tuning,”International Conference on Learning Representations, 2024

    2024

  12. [20]

    Denoising diffusion implicit models,

    Jiaming Song, Chenlin Meng, and Stefano Ermon, “Denoising diffusion implicit models,” in9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. 2021, OpenReview.net

    2021

  13. [21]

    Classifier-free diffusion guid- ance,

    Jonathan Ho and Tim Salimans, “Classifier-free diffusion guid- ance,”arXiv preprint arXiv:2207.12598, 2022

    2022 arXiv

  14. [22]

    Diffusion models beat gans on image synthesis,

    Prafulla Dhariwal and Alexander Nichol, “Diffusion models beat gans on image synthesis,”Advances in neural information processing systems, vol. 34, pp. 8780–8794, 2021

    2021

  15. [23]

    Dense text-to-image generation with attention mod- ulation,

    Yunji Kim, Jiyoung Lee, Jin-Hwa Kim, Jung-Woo Ha, and Jun- Yan Zhu, “Dense text-to-image generation with attention mod- ulation,” inProceedings of the IEEE/CVF International Con- ference on Computer Vision, 2023, pp. 7701–7711

    2023

  16. [24]

    A benchmark dataset and evaluation methodology for video object segmentation,

    F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung, “A benchmark dataset and evaluation methodology for video object segmentation,” inComputer Vision and Pattern Recognition, 2016

    2016

  17. [25]

    The 4th large-scale video object segmentation challenge - video instance segmen- tation track,

    Linjie Yang, Yuchen Fan, and Ning Xu, “The 4th large-scale video object segmentation challenge - video instance segmen- tation track,” June 2022

    2022

  18. [26]

    Sam 3: Segment anything with concepts,

    Nicolas Carion, Laura Gustafson, Yuan-Ting Hu, Shoub- hik Debnath, Ronghang Hu, Didac Suris, Chaitanya Ryali, Kalyan Vasudev Alwala, Haitham Khedr, Andrew Huang, Jie Lei, Tengyu Ma, Baishan Guo, Arpit Kalla, Markus Marks, Joseph Greer, Meng Wang, Peize Sun, Roman R ¨adle, Tri-...

    2025

  19. [27]

    VBench++: Comprehensive and versatile benchmark suite for video gen- erative models,

    Ziqi Huang, Fan Zhang, Xiaojie Xu, Yinan He, Jiashuo Yu, Ziyue Dong, Qianli Ma, Nattapol Chanpaisit, Chenyang Si, Yuming Jiang, Yaohui Wang, Xinyuan Chen, Ying-Cong Chen, Limin Wang, Dahua Lin, Yu Qiao, and Ziwei Liu, “VBench++: Comprehensive and versatile benchmark suite for ...

    2025

  20. [28]

    Learning transferable visual models from nat- ural language supervision,

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever, “Learning transferable visual models from nat- ural language supervision,” inProceedings of...

    2021

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Reviewed June 26, 2026 · model on record in the stance chip above.