arxiv: 2512.07469 · v2 · submitted 2025-12-08 · 💻 cs.CV

Recognition: no theorem link

VideoCoF: Unified Video Editing with Temporal Reasoner

Xiangpeng Yang , Ji Xie , Yiyuan Yang , Yue Ma , Yan Huang , Min Xu , Qiang Wu

Authors on Pith no claims yet

Pith reviewed 2026-05-17 00:05 UTC · model grok-4.3

classification 💻 cs.CV

keywords video editingdiffusion modelschain of framestemporal reasoningmask-free editingunified video modelsregion alignment

0 comments

The pith

Forcing a video diffusion model to predict edit-region latents first produces precise mask-free video editing.

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

The paper presents VideoCoF as a way to unify video editing tasks that currently split between precise but mask-dependent expert models and flexible but spatially vague in-context learners. It does so by inserting an explicit reasoning step inside the diffusion process: the model must output reasoning tokens that identify edit regions before it generates the final edited video tokens. This sequence is trained on only 50,000 video pairs yet reaches state-of-the-art results on the authors' VideoCoF-Bench. A RoPE alignment module then uses those same reasoning tokens to preserve motion consistency and support videos longer than the training length. If the core procedure holds, video editing becomes both more accurate and more accessible without requiring users to supply masks or task-specific priors.

Core claim

VideoCoF enforces a see-reason-then-edit procedure inside a video diffusion model by compelling it to predict reasoning tokens (edit-region latents) before generating target video tokens, thereby achieving precise instruction-to-region alignment without user-provided masks while supporting unified editing across tasks.

What carries the argument

The Chain-of-Frames procedure that inserts a reasoning step to predict edit-region latents prior to target video token generation.

If this is right

Video editing becomes unified across tasks without separate models or user masks.
State-of-the-art results appear on VideoCoF-Bench after training on only 50k video pairs.
Motion alignment and length extrapolation improve through RoPE applied to the reasoning tokens.
The same explicit reasoning step can be added to other diffusion pipelines for finer spatial control.

Where Pith is reading between the lines

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

The reasoning-token stage could be reused in image or 3D editing diffusion models to reduce reliance on masks.
Training cost drops further if the reasoning tokens are distilled from a smaller teacher model rather than learned from scratch.
Iterative editing becomes feasible by feeding the first round's reasoning tokens back as additional context.

Load-bearing premise

Predicting reasoning tokens for edit regions before target video tokens will produce accurate instruction-to-region alignment without masks or extra supervision.

What would settle it

Running the model on videos whose instructions point to visually ambiguous regions and measuring whether edit localization matches ground-truth region accuracy would directly test the claimed alignment gain.

Figures

Figures reproduced from arXiv: 2512.07469 by Ji Xie, Min Xu, Qiang Wu, Xiangpeng Yang, Yan Huang, Yiyuan Yang, Yue Ma.

**Figure 1.** Figure 1: VideoCoF’s video editing capabilities emerge from its seeing, reasoning, then editing framework. Trained on only 50k data (33 frames), this teaser shows multi-instance editing and robust 4× length generalization. Abstract Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temporal i… view at source ↗

**Figure 2.** Figure 2: Illustration of the difference between previous methods and our VideoCoF. We enhances the editing accuracy by forcing the video diffusion model to first predict the editing area, and then perform the editing. ing for the reasoning latent, we reset the temporal indices of the edited video’s rotary position embeddings to match those of the source video, ensuring motion alignment and length extrapolation. To… view at source ↗

**Figure 3.** Figure 3: Overview of VideoCoF framework. Our model processes source (blue), reasoning (orange), and target (green) tokens in a unified sequence to “reason” then “edit”. Bottom right: Our RoPE design enables length extrapolation. [19] concatenate video conditions along the temporal axis to perform ICL. However, these methods are often limited by mask requirements [43] or, as we identify, suffer from fundamental iss… view at source ↗

**Figure 4.** Figure 4: How our RoPE design avoid index collision. for the reasoning frames, teaching the diffusion model to reason about where the edit should occur. Consequently, the entire video editing task is reformulated as a chained process: first “seeing” the original video, then “reasoning” by predicting the grounding region, and finally “editing” to generate the new video content within that specified area. We call thi… view at source ↗

**Figure 5.** Figure 5: Our data curation pipeline for multi-instance data. in per-frame form as L = 1 L + F 2F X +L−1 i=F [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

**Figure 6.** Figure 6: Visual comparision between our VideoCoF and other methods on diverse video editing tasks. Senorita [ ˜ 51] and are 33 frames long, we only training on 50k curated video data finally. Thanks to our RoPE alignment design, the model generalizes to longer sequences at inference (e.g., 141 frames and above). By default we use 33 frames source video, 33 frames edited video, and 4 frames reasoning clip. We train… view at source ↗

**Figure 7.** Figure 7: Length exploration on frames more than training. jects; other methods either fail to edit or mistakenly edit a bowl. These qualitative examples demonstrate VideoCoF’s stronger instance-level reasoning and higher editing fidelity. 4.4. Ablation Study To verify our novel Chain of Frames (CoF) design, particularly its “reasoning frames” and the RoPE design for length exploration, we conduct an ablation study… view at source ↗

**Figure 8.** Figure 8: Motion alignment benefit by our rope design. RoPE Design for Motion Alignment. Setting the temporal index for the reasoning frame latent is a critical design choice. A naive approach is to set its index to 0, aligning it with the first video frame. This causes two severe issues. First, it leads to significant motion misalignment (e.g., the subject fails to perform the ”lifting clothes” motion in [PITH_FU… view at source ↗

**Figure 9.** Figure 9: Ablation on reasoning frame format. 5. Conclusion In this paper, we introduced VideoCoF, a unified model for universal video editing via temporal reasoning. We identified that existing temporal in-context learning approaches often fail due to a lack of explicit spatial cues, leading to weak instruction-to-region mapping and imprecise localization. To address these issues, we proposed the innovative Chain… view at source ↗

**Figure 10.** Figure 10: Input Prompt Variants for In-Context Video Editing. We evaluate two prompt formats: (a) Temporal Triptych Prompt - instructions embedded in structure ”A video sequence showing three parts: first the original scene, then grounded {ground instruction}, and finally the same scene but {edit instruction}.”) (b) Direct Instruction - explicit editing commands provided directly; describe the evolution of video c… view at source ↗

read the original abstract

Existing video editing methods face a critical trade-off: expert models offer precision but rely on task-specific priors like masks, hindering unification; conversely, unified temporal in-context learning models are mask-free but lack explicit spatial cues, leading to weak instruction-to-region mapping and imprecise localization. To resolve this conflict, we propose VideoCoF, a novel Chain-of-Frames approach inspired by Chain-of-Thought reasoning. VideoCoF enforces a ``see, reason, then edit" procedure by compelling the video diffusion model to first predict reasoning tokens (edit-region latents) before generating the target video tokens. This explicit reasoning step removes the need for user-provided masks while achieving precise instruction-to-region alignment and fine-grained video editing. Furthermore, we introduce a RoPE alignment strategy that leverages these reasoning tokens to ensure motion alignment and enable length extrapolation beyond the training duration. We demonstrate that with a minimal data cost of only 50k video pairs, VideoCoF achieves state-of-the-art performance on VideoCoF-Bench, validating the efficiency and effectiveness of our approach. Our code, weight, data are available at https://github.com/knightyxp/VideoCoF.

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.

Desk Editor's Note private letter to a colleague

VideoCoF inserts a reasoning token prediction step before video generation in diffusion editing to drop masks, but the supervision for those tokens looks underspecified.

read the letter

VideoCoF's main move is to make the diffusion model first output reasoning tokens that stand for edit regions, then generate the target video tokens. This Chain-of-Frames procedure plus the RoPE alignment is meant to give precise instruction-to-region mapping without user masks while keeping the model unified across editing tasks. They report this works with only 50k video pairs and reaches SOTA on their VideoCoF-Bench, plus it supports length extrapolation through the RoPE trick. That framing of the precision-versus-unification trade-off is clear and the procedural change is easy to understand. The RoPE part for motion consistency is a practical addition on top of the core idea. The soft spot is exactly where the stress-test points: the abstract describes compelling the model to predict the reasoning tokens first but gives no sign of an auxiliary loss, region reconstruction term, or contrastive signal that would tie those tokens to actual edit locations. Without that, the tokens could function as extra conditioning features rather than explicit spatial reasoning, which would weaken the claimed unification benefit. The SOTA numbers and ablation details are not visible in the provided text, so the central claim stays hard to verify. This is for people building diffusion models for video editing who care about mask-free approaches. The idea is concrete enough and the data claim modest enough that it deserves a serious referee to check the training procedure and the actual contribution of the reasoning step.

Referee Report

2 major / 2 minor

Summary. The paper proposes VideoCoF, a Chain-of-Frames (CoF) method for unified video editing in diffusion models. It enforces a 'see, reason, then edit' procedure by requiring the model to first predict reasoning tokens (edit-region latents) before target video tokens, eliminating the need for user-provided masks while achieving precise instruction-to-region alignment. The approach uses only 50k video pairs for training, introduces a RoPE alignment strategy for motion consistency and length extrapolation, and reports state-of-the-art results on the introduced VideoCoF-Bench.

Significance. If the reasoning-token mechanism demonstrably enforces spatial localization from instructions alone without auxiliary supervision or masks, the work would meaningfully bridge the precision of mask-based expert models with the unification of in-context learning approaches, while highlighting data-efficient training for temporal video tasks.

major comments (2)

[Method / Training Procedure] The central claim that predicting reasoning tokens (edit-region latents) before target tokens produces precise instruction-to-region alignment without masks or additional supervision is not supported by any described auxiliary loss, reconstruction objective, or contrastive term in the method. Without such a mechanism, the tokens risk functioning as generic conditioning rather than explicit spatial reasoning, undermining the 'see, reason, then edit' unification benefit.
[Experiments / Ablations] No details are provided on how the reasoning tokens are trained or supervised (e.g., loss formulation, initialization, or gradient flow from the edit-region latents), nor are ablation studies shown isolating their contribution versus standard diffusion conditioning. This leaves the SOTA claim on VideoCoF-Bench unverifiable from the reported evidence.

minor comments (2)

[Introduction / Experiments] The abstract and method description introduce VideoCoF-Bench without clarifying its construction, diversity, or comparison to existing benchmarks (e.g., how edit instructions and ground-truth regions are sourced).
[Method] Notation for 'reasoning tokens' and 'edit-region latents' is used interchangeably without an explicit definition or diagram showing their dimensionality and integration into the diffusion U-Net.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on VideoCoF. The comments highlight the need for greater clarity on how the reasoning tokens enforce spatial alignment. We address each point below with explanations drawn directly from the method and commit to revisions that add the requested details without altering the core claims.

read point-by-point responses

Referee: [Method / Training Procedure] The central claim that predicting reasoning tokens (edit-region latents) before target tokens produces precise instruction-to-region alignment without masks or additional supervision is not supported by any described auxiliary loss, reconstruction objective, or contrastive term in the method. Without such a mechanism, the tokens risk functioning as generic conditioning rather than explicit spatial reasoning, undermining the 'see, reason, then edit' unification benefit.

Authors: The 'see, reason, then edit' procedure is enforced architecturally by structuring the token sequence so that edit-region latents must be predicted first, before target video tokens, within the diffusion denoising steps. This ordering, combined with the RoPE alignment strategy that conditions motion on the reasoning tokens, encourages the latents to capture instruction-specific regions. No separate auxiliary loss is used; supervision occurs via the standard diffusion denoising objective applied to the full sequence, with gradients flowing from target-token prediction back through the reasoning tokens. We acknowledge that the manuscript does not explicitly detail this gradient flow or sequence construction and will add a dedicated subsection in the revised Method section describing the token ordering, loss application, and how it produces instruction-to-region alignment without masks. revision: yes
Referee: [Experiments / Ablations] No details are provided on how the reasoning tokens are trained or supervised (e.g., loss formulation, initialization, or gradient flow from the edit-region latents), nor are ablation studies shown isolating their contribution versus standard diffusion conditioning. This leaves the SOTA claim on VideoCoF-Bench unverifiable from the reported evidence.

Authors: The reasoning tokens are trained jointly with the target tokens under the standard DDPM denoising loss; they are initialized from a lightweight encoder applied to the instruction and noisy latents. We will revise the Experiments section to explicitly state the loss formulation, initialization procedure, and gradient flow. We will also add ablation studies that compare the full VideoCoF model against a baseline using only standard diffusion conditioning (i.e., removing the explicit reasoning-token prediction step) while keeping all other components fixed, reporting quantitative results on VideoCoF-Bench to isolate the contribution. These additions will make the SOTA claims directly verifiable. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents VideoCoF as a procedural modification to video diffusion models, where reasoning tokens (edit-region latents) are predicted before target video tokens to enforce a 'see, reason, then edit' sequence. This is described as an explicit ordering constraint on standard diffusion conditioning, with RoPE alignment added for motion consistency. No equations, derivations, or fitted parameters are shown that reduce the claimed instruction-to-region alignment or SOTA results on VideoCoF-Bench to tautological inputs or self-citations. The 50k video pair training cost and mask-free unification are presented as empirical outcomes rather than mathematical necessities derived from prior self-work. The approach builds on external diffusion and Chain-of-Thought concepts without load-bearing self-citation chains or ansatzes that collapse the central claim.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The approach rests on standard assumptions of video diffusion models plus the new procedural requirement that reasoning tokens can be learned to capture spatial edit cues.

axioms (1)

domain assumption Video diffusion models can be extended to first predict auxiliary reasoning tokens that encode edit regions
Invoked in the description of the see-reason-edit procedure.

invented entities (1)

reasoning tokens (edit-region latents) no independent evidence
purpose: Provide explicit spatial cues for instruction-to-region mapping without masks
New entity introduced to enforce the chain-of-frames reasoning step

pith-pipeline@v0.9.0 · 5514 in / 1118 out tokens · 39344 ms · 2026-05-17T00:05:04.414346+00:00 · methodology

discussion (0)

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

MiVE: Multiscale Vision-language features for reference-guided video Editing
cs.CV 2026-05 unverdicted novelty 7.0

MiVE repurposes VLMs as multiscale feature extractors integrated into a unified self-attention Diffusion Transformer, achieving top human preference in reference-guided video editing.
LIVE: Leveraging Image Manipulation Priors for Instruction-based Video Editing
cs.CV 2026-04 unverdicted novelty 6.0

LIVE achieves state-of-the-art instruction-based video editing by jointly training on image and video data with a frame-wise token noise strategy to bridge domain gaps and a new benchmark of over 60 tasks.
Measuring AI Reasoning: A Guide for Researchers
cs.AI 2026-05 unverdicted novelty 4.0

Reasoning in language models should be measured by the faithfulness and validity of their multi-step search processes and intermediate traces, not final-answer accuracy.

Reference graph

Works this paper leans on

55 extracted references · 55 canonical work pages · cited by 3 Pith papers · 9 internal anchors

[1]

Videopainter: Any- length video inpainting and editing with plug-and-play con- text control.arXiv preprint arXiv:2503.05639, 2025

Yuxuan Bian, Zhaoyang Zhang, Xuan Ju, Mingdeng Cao, Liangbin Xie, Ying Shan, and Qiang Xu. Videopainter: Any- length video inpainting and editing with plug-and-play con- text control.arXiv preprint arXiv:2503.05639, 2025. 2

work page arXiv 2025
[2]

In- structpix2pix: Learning to follow image editing instructions

Tim Brooks, Aleksander Holynski, and Alexei A Efros. In- structpix2pix: Learning to follow image editing instructions. InProceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 18392–18402, 2023. 2, 6, 4

work page 2023
[3]

Emerg- ing properties in self-supervised vision transformers

Mathilde Caron, Hugo Touvron, Ishan Misra, Herv ´e J´egou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerg- ing properties in self-supervised vision transformers. InPro- ceedings of the IEEE/CVF international conference on com- puter vision, pages 9650–9660, 2021. 3

work page 2021
[4]

Univid: Unifying vi- sion tasks with pre-trained video generation models.arXiv preprint arXiv:2509.21760, 2025

Lan Chen, Yuchao Gu, and Qi Mao. Univid: Unifying vi- sion tasks with pre-trained video generation models.arXiv preprint arXiv:2509.21760, 2025. 3

work page arXiv 2025
[5]

Consistent video- to-video transfer using synthetic dataset.arXiv preprint arXiv:2311.00213, 2023

Jiaxin Cheng, Tianjun Xiao, and Tong He. Consistent video- to-video transfer using synthetic dataset.arXiv preprint arXiv:2311.00213, 2023. 2, 6, 4

work page arXiv 2023
[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. InPro- ceedings of the International Conference on Learning Rep- resentations (ICLR), 2024. 2

work page 2024
[7]

Videoswap: Customized video subject swapping with interactive semantic point cor- respondence

Yuchao Gu, Yipin Zhou, Bichen Wu, Licheng Yu, Jia-Wei Liu, Rui Zhao, Jay Zhangjie Wu, David Junhao Zhang, Mike Zheng Shou, and Kevin Tang. Videoswap: Customized video subject swapping with interactive semantic point cor- respondence. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7621– 7630, 2024. 2

work page 2024
[8]

Diffusion with offset noise.https: //www.crosslabs.org/blog/diffusion-with- offset-noise, 2023

Nicholas Guttenberg. Diffusion with offset noise.https: //www.crosslabs.org/blog/diffusion-with- offset-noise, 2023. 3

work page 2023
[9]

In-context lora for diffusion transformers.arXiv preprint arxiv:2410.23775, 2024

Lianghua Huang, Wei Wang, Zhi-Fan Wu, Yupeng Shi, Huanzhang Dou, Chen Liang, Yutong Feng, Yu Liu, and Jin- gren Zhou. In-context lora for diffusion transformers.arXiv preprint arxiv:2410.23775, 2024. 1

work page arXiv 2024
[10]

VACE: All-in-One Video Creation and Editing

Zeyinzi Jiang, Zhen Han, Chaojie Mao, Jingfeng Zhang, Yulin Pan, and Yu Liu. Vace: All-in-one video creation and editing.arXiv preprint arXiv:2503.07598, 2025. 5, 6, 2, 4

work page internal anchor Pith review Pith/arXiv arXiv 2025
[11]

EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning

Xuan Ju, Tianyu Wang, Yuqian Zhou, He Zhang, Qing Liu, Nanxuan Zhao, Zhifei Zhang, Yijun Li, Yuanhao Cai, Shaoteng Liu, et al. Editverse: Unifying image and video editing and generation with in-context learning.arXiv preprint arXiv:2509.20360, 2025. 2, 3

work page internal anchor Pith review Pith/arXiv arXiv 2025
[12]

Fulldit: Multi-task video generative foundation model with full attention.arXiv preprint arXiv:2503.19907, 2025

Xuan Ju, Weicai Ye, Quande Liu, Qiulin Wang, Xintao Wang, Pengfei Wan, Di Zhang, Kun Gai, and Qiang Xu. Fulldit: Multi-task video generative foundation model with full attention.arXiv preprint arXiv:2503.19907, 2025. 2

work page arXiv 2025
[13]

Large language models are zero-shot reasoners.Advances in neural information pro- cessing systems, 35:22199–22213, 2022

Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. Large language models are zero-shot reasoners.Advances in neural information pro- cessing systems, 35:22199–22213, 2022. 3

work page 2022
[14]

HunyuanVideo: A Systematic Framework For Large Video Generative Models

Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models.arXiv preprint arXiv:2412.03603, 2024. 2

work page internal anchor Pith review Pith/arXiv arXiv 2024
[15]

Viescore: Towards explainable metrics for conditional image synthesis evaluation, 2023

Max Ku, Dongfu Jiang, Cong Wei, Xiang Yue, and Wenhu Chen. Viescore: Towards explainable metrics for conditional image synthesis evaluation, 2023. 5

work page 2023
[16]

Nohumansrequired: Autonomous high-quality image editing triplet mining.arXiv preprint arXiv:2507.14119, 2025

Maksim Kuprashevich, Grigorii Alekseenko, Irina Tolstykh, Georgii Fedorov, Bulat Suleimanov, Vladimir Dokholyan, and Aleksandr Gordeev. Nohumansrequired: Autonomous high-quality image editing triplet mining.arXiv preprint arXiv:2507.14119, 2025. 3

work page arXiv 2025
[17]

Five: A fine-grained video editing bench- mark for evaluating emerging diffusion and rectified flow models.arXiv preprint arXiv:2503.13684, 2025

Minghan Li, Chenxi Xie, Yichen Wu, Lei Zhang, and Mengyu Wang. Five: A fine-grained video editing bench- mark for evaluating emerging diffusion and rectified flow models.arXiv preprint arXiv:2503.13684, 2025. 6

work page arXiv 2025
[18]

Dif- fueraser: A diffusion model for video inpainting.arXiv preprint arXiv:2501.10018, 2025

Xiaowen Li, Haolan Xue, Peiran Ren, and Liefeng Bo. Dif- fueraser: A diffusion model for video inpainting.arXiv preprint arXiv:2501.10018, 2025. 2

work page arXiv 2025
[19]

In-context learning with unpaired clips for instruction-based video editing.arXiv preprint arXiv:2510.14648, 2025

Xinyao Liao, Xianfang Zeng, Ziye Song, Zhoujie Fu, Gang Yu, and Guosheng Lin. In-context learning with unpaired clips for instruction-based video editing.arXiv preprint arXiv:2510.14648, 2025. 2, 3, 6, 7, 1, 4

work page arXiv 2025
[20]

Decoupled Weight Decay Regularization

Ilya Loshchilov, Frank Hutter, et al. Fixing weight decay regularization in adam.arXiv preprint arXiv:1711.05101, 5 (5):5, 2017. 6

work page internal anchor Pith review Pith/arXiv arXiv 2017
[21]

In- structx: Towards unified visual editing with mllm guidance

Chong Mou, Qichao Sun, Yanze Wu, Pengze Zhang, Xinghui Li, Fulong Ye, Songtao Zhao, and Qian He. In- structx: Towards unified visual editing with mllm guidance. arXiv preprint arXiv:2510.08485, 2025. 2, 3

work page arXiv 2025
[22]

Hello gpt-4o

OpenAI. Hello gpt-4o. Blog post, 2024. 5, 7, 2

work page 2024
[23]

Pexels: Free stock photos, royalty free stock images & videos.https://www.pexels.com/, 2025

Pexels. Pexels: Free stock photos, royalty free stock images & videos.https://www.pexels.com/, 2025. Ac- cessed: 2025-11-06. 5, 6, 2

work page 2025
[24]

Fatezero: Fus- ing attentions for zero-shot text-based video editing

Chenyang Qi, Xiaodong Cun, Yong Zhang, Chenyang Lei, Xintao Wang, Ying Shan, and Qifeng Chen. Fatezero: Fus- ing attentions for zero-shot text-based video editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15932–15942, 2023. 2

work page 2023
[25]

Learning transferable visual models from natural language supervi- sion

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. InInternational conference on machine learning, pages 8748–8763. PmLR, 2021. 3

work page 2021
[26]

Grounded sam: Assembling open-world models for diverse visual tasks,

Tianhe Ren, Shilong Liu, Ailing Zeng, Jing Lin, Kun- chang Li, He Cao, Jiayu Chen, Xinyu Huang, Yukang Chen, Feng Yan, Zhaoyang Zeng, Hao Zhang, Feng Li, Jie Yang, Hongyang Li, Qing Jiang, and Lei Zhang. Grounded sam: Assembling open-world models for diverse visual tasks,

work page
[27]

Large-scale text-to-image model with inpainting is a zero-shot subject-driven image generator

Chaehun Shin, Jooyoung Choi, Heeseung Kim, and Sungroh Yoon. Large-scale text-to-image model with inpainting is a zero-shot subject-driven image generator. 2024. 1

work page 2024
[28]

Roformer: Enhanced transformer with 9 rotary position embedding.Neurocomputing, 568:127063,

Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with 9 rotary position embedding.Neurocomputing, 568:127063,

work page
[29]

Diffusion model-based video editing: A survey.arXiv preprint arXiv:2407.07111, 2024

Wenhao Sun, Rong-Cheng Tu, Jingyi Liao, and Dacheng Tao. Diffusion model-based video editing: A survey.arXiv preprint arXiv:2407.07111, 2024. 6

work page arXiv 2024
[30]

Lucy edit: Open-weight text-guided video editing

DecartAI Team. Lucy edit: Open-weight text-guided video editing. 2025. 6, 7, 4

work page 2025
[31]

Videoanydoor: High-fidelity video ob- ject insertion with precise motion control

Yuanpeng Tu, Hao Luo, Xi Chen, Sihui Ji, Xiang Bai, and Hengshuang Zhao. Videoanydoor: High-fidelity video ob- ject insertion with precise motion control. InProceedings of the Special Interest Group on Computer Graphics and In- teractive Techniques Conference Conference Papers, pages 1–11, 2025. 2

work page 2025
[32]

Wan: Open and Advanced Large-Scale Video Generative Models

Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video gen- erative models.arXiv preprint arXiv:2503.20314, 2025. 2, 3, 5

work page internal anchor Pith review Pith/arXiv arXiv 2025
[33]

Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Jun- yang Lin. Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.arXiv preprint arXiv:2409.12191, 2024. 5

work page internal anchor Pith review Pith/arXiv arXiv 2024
[34]

Univideo: Unified understanding, generation, and editing for videos

Cong Wei, Quande Liu, Zixuan Ye, Qiulin Wang, Xintao Wang, Pengfei Wan, Kun Gai, and Wenhu Chen. Univideo: Unified understanding, generation, and editing for videos. arXiv preprint arXiv:2510.08377, 2025. 2

work page arXiv 2025
[35]

Chain-of-thought prompting elicits reasoning in large lan- guage models.Advances in neural information processing systems, 35:24824–24837, 2022

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large lan- guage models.Advances in neural information processing systems, 35:24824–24837, 2022. 2, 3

work page 2022
[36]

Video models are zero-shot learners and reasoners

Thadd ¨aus Wiedemer, Yuxuan Li, Paul Vicol, Shixiang Shane Gu, Nick Matarese, Kevin Swersky, Been Kim, Priyank Jaini, and Robert Geirhos. Video models are zero-shot learn- ers and reasoners.arXiv preprint arXiv:2509.20328, 2025. 3

work page internal anchor Pith review Pith/arXiv arXiv 2025
[37]

Exploring video quality assessment on user generated contents from aesthetic and technical perspectives

Haoning Wu, Erli Zhang, Liang Liao, Chaofeng Chen, Jing- wen Hou Hou, Annan Wang, Wenxiu Sun Sun, Qiong Yan, and Weisi Lin. Exploring video quality assessment on user generated contents from aesthetic and technical perspectives. InInternational Conference on Computer Vision (ICCV),

work page
[38]

Tune-a-video: One-shot tuning of image 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 image diffusion models for text-to-video generation. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7623–7633, 2023. 2

work page 2023
[39]

Cvpr 2023 text guided video edit- ing competition.arXiv preprint arXiv:2310.16003, 2023

Jay Zhangjie Wu, Xiuyu Li, Difei Gao, Zhen Dong, Jin- bin Bai, Aishani Singh, Xiaoyu Xiang, Youzeng Li, Zuwei Huang, Yuanxi Sun, et al. Cvpr 2023 text guided video edit- ing competition.arXiv preprint arXiv:2310.16003, 2023. 6

work page arXiv 2023
[40]

Omnigen: Unified image genera- tion

Shitao Xiao, Yueze Wang, Junjie Zhou, Huaying Yuan, Xin- grun Xing, Ruiran Yan, Chaofan Li, Shuting Wang, Tiejun Huang, and Zheng Liu. Omnigen: Unified image genera- tion. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 13294–13304, 2025. 2

work page 2025
[41]

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. 2

work page 2025
[42]

CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiao- han Zhang, Guanyu Feng, et al. Cogvideox: Text-to-video diffusion models with an expert transformer.arXiv preprint arXiv:2408.06072, 2024. 2

work page internal anchor Pith review Pith/arXiv arXiv 2024
[43]

Unic: Unified in-context video editing.arXiv preprint arXiv:2506.04216, 2025

Zixuan Ye, Xuanhua He, Quande Liu, Qiulin Wang, Xintao Wang, Pengfei Wan, Di Zhang, Kun Gai, Qifeng Chen, and Wenhan Luo. Unic: Unified in-context video editing.arXiv preprint arXiv:2506.04216, 2025. 2, 3

work page arXiv 2025
[44]

Stylemaster: Stylize your video with artistic generation and translation

Zixuan Ye, Huijuan Huang, Xintao Wang, Pengfei Wan, Di Zhang, and Wenhan Luo. Stylemaster: Stylize your video with artistic generation and translation. InProceedings of the Computer Vision and Pattern Recognition Conference, pages 2630–2640, 2025. 2

work page 2025
[45]

Veg- gie: Instructional editing and reasoning video concepts with grounded generation

Shoubin Yu, Difan Liu, Ziqiao Ma, Yicong Hong, Yang Zhou, Hao Tan, Joyce Chai, and Mohit Bansal. Veg- gie: Instructional editing and reasoning video concepts with grounded generation. InProceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 15147– 15158, 2025. 2, 8

work page 2025
[46]

Magicbrush: A manually annotated dataset for instruction- guided image editing.Advances in Neural Information Pro- cessing Systems, 36:31428–31449, 2023

Kai Zhang, Lingbo Mo, Wenhu Chen, Huan Sun, and Yu Su. Magicbrush: A manually annotated dataset for instruction- guided image editing.Advances in Neural Information Pro- cessing Systems, 36:31428–31449, 2023. 3

work page 2023
[47]

Adding conditional control to text-to-image diffusion models

Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3836–3847, 2023. 2

work page 2023
[49]

In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformer

Zechuan Zhang, Ji Xie, Yu Lu, Zongxin Yang, and Yi Yang. In-context edit: Enabling instructional image editing with in- context generation in large scale diffusion transformer.arXiv preprint arXiv:2504.20690, 2025. 2

work page internal anchor Pith review Pith/arXiv arXiv 2025
[50]

Minimax-remover: Taming bad noise helps video object removal.arXiv preprint arXiv:2505.24873, 2025

Bojia Zi, Weixuan Peng, Xianbiao Qi, Jianan Wang, Shihao Zhao, Rong Xiao, and Kam-Fai Wong. Minimax-remover: Taming bad noise helps video object removal.arXiv preprint arXiv:2505.24873, 2025. 2, 5

work page arXiv 2025
[51]

Se\˜ norita-2m: A high-quality instruction- based dataset for general video editing by video specialists

Bojia Zi, Penghui Ruan, Marco Chen, Xianbiao Qi, Shaozhe Hao, Shihao Zhao, Youze Huang, Bin Liang, Rong Xiao, and Kam-Fai Wong. Se\˜ norita-2m: A high-quality instruction- based dataset for general video editing by video specialists. arXiv preprint arXiv:2502.06734, 2025. 5, 6, 4 10 Unified Video Editing with Temporal Reasoner Supplementary Material This ...

work page arXiv 2025
[52]

7, we provide a detailed breakdown of the results across four distinct tasks: Object Removal, Object Addition, Object Swap, and Local Style Transfer

Full Comparison As shown in Tab. 7, we provide a detailed breakdown of the results across four distinct tasks: Object Removal, Object Addition, Object Swap, and Local Style Transfer. Our VideoCoF consistently achieves the highest scores in instruction followingandsuccess ratioacross all tasks, demonstrating superior capability in understanding and exe- cu...

work page
[53]

A video sequence showing three parts: first the original scene, then grounded{ground instruction}, and finally the same scene but{edit instruc- tion}

More Ablation Studies In this section, we validate key design choices of VideoCoF: the length of reasoning frames and the dispatch prompt. 7.1. Ablation on Reasoning Frames Tab. 4 investigates the optimal number of reasoning frames (F) for spatial guidance. Considering the VideoV AE tem- poral compression formulaL= (F−1)//4 + 1, frames 1∼4map to a single ...

work page arXiv 1918
[54]

Training Dataset To equip our model with robust instruction-following ca- pabilities, we constructed a unified chain-of-frames video editing dataset comprising 50k video pairs

Implementation Details 8.1. Training Dataset To equip our model with robust instruction-following ca- pabilities, we constructed a unified chain-of-frames video editing dataset comprising 50k video pairs. As detailed in Table 6, the dataset is strategically balanced across four core editing tasks: object addition, removal, swapping, and local stylization....

work page
[55]

Metrics GPT Evaluation.To comprehensively assess the edit- ing performance, we employ the state-of-the-art Vision- Language Model, GPT-4o [22], serving as an automated judge. Following the protocol of InstructX [21], we sam- ple three frames from each video pair and utilize structured 2 prompts in [21] to evaluate the results across the following dimensio...

work page
[56]

reasoning-then-editing

Discussion Scaling up Chain-of-Frames.Currently, Video- CoF achieves SOTA performance in instruction following and success rate using only 50k source-reasoning-editing pairs. This demonstrates remarkable data efficiency compared to existing large-scale baselines. For instance, EditVerse [11] utilizes 4M videos and 8M images, ICVE [19] leverages 2M pre-tra...

work page