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REVIEW 3 major objections 4 minor 27 references

IN2OUT: Fine-Tuning Video Inpainting Model for Video Outpainting Using Hierarchical Discriminator

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper argues that video inpainting models can be fine-tuned for video outpainting when the discriminator judges both local detail and global coherence.

desk verdict Promising new recipe for adapting inpainting models to outpainting, but the version we have is missing the method and experiments, so its central causal claim is unverifiable. read the letter →

arxiv 2508.00418 v1 pith:IPOIKKVA submitted 2025-08-01 cs.CV eess.IV

Sangwoo Youn , Minji Lee , Nokap Tony Park , Yeonggyoo Jeon , Taeyoung Na This is my paper · ORCID
classification cs.CVeess.IV
keywords videooutpaintinginpaintinghierarchicaldiscriminatoradversarialtrainingfine-tuningglobalcoherencelocaltextureflow-guidedpropagation
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 argues that video inpainting models, which already learn object flow and reconstruction, can be repurposed for video outpainting if the fine-tuning uses the right discriminator. Directly fine-tuning produces blurry extended borders; the authors trace the blur to the adversarial loss inherited from inpainting training rather than to the outpainting task itself. They introduce a hierarchical discriminator that scores local patch quality in early layers and global frame coherence in deeper layers, together with a loss that reads both local and global features from real and generated videos. Fine-tuned with this objective, a ProPainter or E2FGVI backbone generates sharp, globally consistent outpainted borders, and the method reports state-of-the-art results.

What carries the argument

The load-bearing component is the hierarchical discriminator built from convolutional layers: early layers respond to local texture and edges, while deeper layers compare patches across frames to judge global consistency. A custom outpainting loss operates on local and global features extracted from both real and generated videos, aligning them during fine-tuning. It plays the role of replacing the standard inpainting adversarial loss that the authors identify as the source of blur, and it is the part that carries the adaptation from inpainting to outpainting.

What would settle it

Fine-tune the same inpainting backbone for outpainting with a strong non-adversarial objective, for example perceptual and flow losses without any discriminator, and measure border sharpness; if the output is as sharp as with the hierarchical discriminator, the discriminator is not the critical missing component.

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Extended reading notes

Core claim

The central claim is that the missing component in outpainting fine-tuning is a discriminator capable of judging the perceptual quality of extended areas at two complementary scales. The proposed hierarchical discriminator separates adversarial objectives into a global goal (whole-scene coherence) and a local goal (detail sharpness in the extended border), and the matching outpainting loss pulls both feature levels toward those of real video. On this account, the generator's existing flow and reconstruction abilities are sufficient once the right training signal is in place; the same discriminator and loss can be attached to any video inpainting model because they are orthogonal to the generator architecture. The paper reports quantitative and qualitative superiority over previous outpainting methods and inpainting baselines.

Load-bearing premise

The paper assumes the blurriness of naively fine-tuned inpainting models comes from the adversarial loss rather than from the generator's limits or from the outpainting task itself; if that attribution is wrong, the hierarchical discriminator cannot deliver the claimed fix.

Editorial extensions

If this is right

  • Any video inpainting model can be adapted to outpainting with this discriminator and loss, independent of generator architecture.
  • Outpainted border regions become noticeably sharper, with less blur than standard adversarial fine-tuning produces.
  • Generated extensions remain globally coherent with the input video, not merely locally plausible.
  • The diagnosis shifts the research target for video outpainting from generator design toward discriminator design.
  • The method achieves state-of-the-art quantitative and qualitative results on video outpainting benchmarks.

Reading between the lines

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

  • If the diagnosis holds, a similar hierarchical discriminator could sharpen other generative tasks where blur appears at image boundaries, such as image outpainting or video retargeting.
  • A testable extension is applying the same fine-tuning recipe to diffusion-based video generators, since the paper only demonstrates it on flow-guided inpainting backbones.
  • The reported static-video failure suggests the method leans on motion cues, so scenes without motion would likely need a stronger prior for unseen foreground content.
  • Because the discriminator and loss are orthogonal to the generator, gains should transfer to newer inpainting models as they appear, provided the features remain convolutional.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. The manuscript proposes IN2OUT, a video outpainting method obtained by fine-tuning a video inpainting model (e.g., ProPainter, E2FGVI) with a new hierarchical discriminator and a corresponding outpainting loss. The authors argue that the blurriness observed when naively fine-tuning inpainting models for outpainting is caused by the adversarial loss/discriminator rather than by the inherent lack of context, and they split adversarial objectives into global and local goals. The abstract claims state-of-the-art quantitative and qualitative results. However, the submitted text contains only the abstract, introduction, limitation section, and references; the method and experimental sections are absent, so the technical content and the evaluation cannot be inspected.

Significance. If the claims are correct, IN2OUT would offer a simple, generator-agnostic way to repurpose strong video inpainting models for outpainting, with a plausible and easily described design of separating local and global discriminator objectives. The stated availability of code and supplementary demo is a positive feature, and the candid acknowledgment of failure cases on static videos is prudent. Nevertheless, the significance cannot be assessed from the submitted text: no architectural details, equations, ablations, datasets, baselines, or error bars are presented, and the state-of-the-art claim is therefore unsupported.

major comments (3)
  1. [Abstract; missing Sections 3 and 5] The central claims of the paper require the method and experiments for evaluation, but the submitted text contains neither. There is no description of the hierarchical discriminator's architecture, no definition of the outpainting loss, no training objective equation, no dataset or metric description, no baseline list, and no quantitative results. The asserted state-of-the-art performance in the Abstract is therefore unverifiable from the manuscript as submitted. This is a load-bearing omission, as every contribution listed in Section 1 (the discriminator design, the adversarial objective, and the SOTA claim) depends on these missing materials.
  2. [Section 1] The paper attributes the blurriness of naive inpainting-to-outpainting fine-tuning to 'the current adversarial loss used in inpainting training' and explicitly rejects Dehan et al.'s attribution to the lack of surrounding information. This causal claim is the motivation for the entire method, but the evidence for it is only referenced as 'extensive experiments on discriminator designs,' which are not included. To support the claim, the authors should provide ablations that hold the generator fixed and vary only the discriminator (including a strong single discriminator as a control) and, to support the claim that the approach is orthogonal to the generator architecture, ablations that vary the generator while holding the discriminator fixed.
  3. [Section 4 (Limitation), Fig. 5] The limitation section states that on static videos the method 'sometimes blurs (left) or omits (right) the foreground that is never seen in a given region.' This is in direct tension with the unqualified 'outperforms state-of-the-art methods' claim if the comparison includes static videos. The paper should state whether the reported quantitative results include static video test cases, provide per-category or per-video breakdowns, and specify how failure cases are counted; otherwise the SOTA claim is too broad.
minor comments (4)
  1. [Index Terms] The index term 'Dicriminator' should be spelled 'Discriminator.'
  2. [Section 1] The phrase 'termed IN2O UT' contains an erroneous space; it should read 'termed IN2OUT.'
  3. [Fig. 5 caption] The dataset name 'DA VIS' appears with an unintended space and should read 'DAVIS.'
  4. [References] Reference [8] contains stray page numbers '5, 1' at the end, and reference [15] similarly ends with a stray '1'; these should be cleaned up.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identified: the proposed hierarchical discriminator and outpainting loss are empirical components whose success is claimed via benchmark comparison, not derived from their own definitions.

full rationale

The supplied text contains no equation-level derivation in which a predicted quantity is defined from the quantity it is supposed to predict, and no fitted parameter is renamed as a prediction. The central proposal is a hierarchical discriminator with local and global objectives plus an outpainting loss using discriminator features; this is standard adversarial fine-tuning machinery whose evaluation is claimed against external baselines ('Our proposed method outperforms state-of-the-art methods both quantitatively and qualitatively'). The causal attribution that blur arises from the inpainting adversarial loss is an empirical hypothesis, not a tautology: it is asserted in Section 1 ('we attribute this to the current adversarial loss used in inpainting training') and would require ablations that vary only the discriminator, which are not present in the submitted excerpt. Missing sections (Method and Experiments) make the claim under-supported, but under-support is not circularity. The Limitation section admits failure on static videos ('Our method sometimes blurs (left) or omits (right) the foreground that is never seen in a given region'), which narrows the claim but does not reduce it to its inputs. No load-bearing self-citations appear: the reference list contains no paper authored by the present authors, and no uniqueness theorem or prior-work ansatz is invoked to force the design. Accordingly, no circular step can be exhibited with the required quote-and-reduction evidence, and the circularity score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities. The central method depends on two empirical hyperparameters (the layer at which the discriminator switches from local to global assessment, and the weighting of adversarial versus reconstruction losses) and on three domain assumptions about CNN feature semantics, preservation of inpainting capabilities during fine-tuning, and the causal diagnosis of blurriness. These are not derived from theory and could vary across datasets.

free parameters (2)
  • discriminator layer split (early vs deep layers)
    The paper says early layers assess local quality and deep layers assess global consistency; the choice of which layer separates local from global is an architectural hyperparameter selected empirically.
  • adversarial vs reconstruction loss weights
    GAN fine-tuning requires balancing reconstruction loss and adversarial loss; values are typically tuned on validation data, and the paper does not state them in the abstract or introduction.
assumptions (3)
  • domain assumption Early convolutional layers of a discriminator assess local patch quality while deep layers assess global scene consistency.
    This premise justifies the hierarchical discriminator design; it is a widely used but heuristic property of CNNs, cited conceptually in the introduction.
  • domain assumption Fine-tuning on the new adversarial loss preserves the flow estimation and reconstruction abilities of the pre-trained inpainting model.
    The applicability of fine-tuning depends on the generator not losing its inpainting strengths; the paper does not freeze or regularize these capabilities.
  • ad hoc to paper The blurriness observed in naive inpainting-to-outpainting fine-tuning is caused exclusively by the adversarial loss / discriminator, not by generator architecture or task mismatch.
    This causal diagnosis is asserted in the introduction ('we attribute this to the current adversarial loss') and is the basis for the proposed fix; it is not proven by a controlled experiment we can see.

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Cite this review

Pith. "Pith review of IN2OUT: Fine-Tuning Video Inpainting Model for Video Outpainting Using Hierarchical Discriminator." pith.science (2026). https://pith.science/paper/IPOIKKVA

@misc{pith2026250800418,
  author       = {Pith},
  title        = {Pith review of: IN2OUT: Fine-Tuning Video Inpainting Model for Video Outpainting Using Hierarchical Discriminator},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IPOIKKVA}},
  note         = {Machine review of arXiv:2508.00418}
}
read the original abstract

Video outpainting presents a unique challenge of extending the borders while maintaining consistency with the given content. In this paper, we suggest the use of video inpainting models that excel in object flow learning and reconstruction in outpainting rather than solely generating the background as in existing methods. However, directly applying or fine-tuning inpainting models to outpainting has shown to be ineffective, often leading to blurry results. Our extensive experiments on discriminator designs reveal that a critical component missing in the outpainting fine-tuning process is a discriminator capable of effectively assessing the perceptual quality of the extended areas. To tackle this limitation, we differentiate the objectives of adversarial training into global and local goals and introduce a hierarchical discriminator that meets both objectives. Additionally, we develop a specialized outpainting loss function that leverages both local and global features of the discriminator. Fine-tuning on this adversarial loss function enhances the generator's ability to produce both visually appealing and globally coherent outpainted scenes. Our proposed method outperforms state-of-the-art methods both quantitatively and qualitatively. Supplementary materials including the demo video and the code are available in SigPort.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

27 extracted references · 24 canonical work pages

  1. [1]

    Image and video outpainting are inherently distinct due to the possible existence of information about the extended region in the other frames of the video

    INTRODUCTION Despite the advances of diffusion models and generative adver- sarial networks, video outpainting has not been as extensively studied as image outpainting. Image and video outpainting are inherently distinct due to the possible existence of information about the extended region in the other frames of the video. In contrast, video inpainting, ...

    work page 2024

  2. [2]

    An investigation, through extensive comparisons, into the failures of commonly-used discriminators in video out- painting, highlighting the critical role of the discriminator during fine-tuning

    work page

  3. [3]

    A novel adversarial objective specifically tailored for video outpainting that reduces blur in outpainted regions

    work page

  4. [4]

    The first successful adaptation of a video inpainting model to the outpainting task

    work page

  5. [5]

    IN2OUT: Fine-Tuning Video Inpainting Model for Video Outpainting Using Hierarchical Discriminator

    Achievement of state-of-the-art performance compared to previous outpainting methods and inpainting baselines. arXiv:2508.00418v1 [cs.CV] 1 Aug 2025

    work page Pith review arXiv 2025

  6. [6]

    Free-form video inpainting with 3d gated convolution and temporal patchgan,

    Ya-Liang Chang, Zhe Yu Liu, Kuan-Ying Lee, and Win- ston Hsu, “Free-form video inpainting with 3d gated convolution and temporal patchgan,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 9066–9075. 1, 2, 5

    work page 2019

  7. [7]

    Towards an end-to-end framework for flow-guided video inpainting,

    Zhen Li, Cheng-Ze Lu, Jianhua Qin, Chun-Le Guo, and Ming-Ming Cheng, “Towards an end-to-end framework for flow-guided video inpainting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 17562–17571. 1, 2, 3, 4, 5

    work page 2022

  8. [8]

    Propainter: Improving propagation and transformer for video inpainting,

    Shangchen Zhou, Chongyi Li, Kelvin CK Chan, and Chen Change Loy, “Propainter: Improving propagation and transformer for video inpainting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 10477–10486. 1, 2, 3, 4

    work page 2023

Show all 27 references
  1. [9]

    Complete and temporally consistent video outpainting,

    Lo¨ıc Dehan, Wiebe Van Ranst, Patrick Vandewalle, and Toon Goedem´e, “Complete and temporally consistent video outpainting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 687–695. 1, 2, 3, 4, 5

    2022

  2. [10]

    Video extrapolation using neighbor- ing frames,

    Sangwoo Lee, Jungjin Lee, Bumki Kim, Kyehyun Kim, and Junyong Noh, “Video extrapolation using neighbor- ing frames,” ACM Transactions on Graphics (TOG), vol. 38, no. 3, pp. 1–13, 2019. 2

    2019

  3. [11]

    Flow-edge guided video completion,

    Chen Gao, Ayush Saraf, Jia-Bin Huang, and Johannes Kopf, “Flow-edge guided video completion,” in Euro- pean Conference on Computer Vision, 2020, pp. 713–729. 2

    2020

  4. [12]

    Object-ratio-preserving video retargeting framework based on segmentation and inpainting,

    Jun-Gyu Jin, Jaehyun Bae, Han-gyul Baek, and Sang- hyo Park, “Object-ratio-preserving video retargeting framework based on segmentation and inpainting,” in Proceedings of the IEEE/CVF Winter Conference on Ap- plications of Computer Vision, 2023, pp. 497–503. 2

    2023

  5. [13]

    Hierarchical masked 3d diffusion model for video outpainting,

    Fanda Fan, Chaoxu Guo, Litong Gong, Biao Wang, Tiezheng Ge, Yuning Jiang, Chunjie Luo, and Jianfeng Zhan, “Hierarchical masked 3d diffusion model for video outpainting,” in Proceedings of the 31st ACM Interna- tional Conference on Multimedia, 2023, pp. 7890–7900. 2, 3, 4, 5, 1

    2023

  6. [14]

    Classifier-free diffusion guidance,

    Jonathan Ho and Tim Salimans, “Classifier-free diffusion guidance,” arXiv preprint arXiv:2207.12598, 2022. 2

    2022 arXiv

  7. [15]

    Be-your-outpainter: Mastering video out- painting through input-specific adaptation,

    Fu-Yun Wang, Xiaoshi Wu, Zhaoyang Huang, Xiaoyu Shi, Dazhong Shen, Guanglu Song, Yu Liu, and Hong- sheng Li, “Be-your-outpainter: Mastering video out- painting through input-specific adaptation,” in European Conference on Computer Vision, 2024, pp. 153–168. 2, 3, 4

    2024

  8. [16]

    Context encoders: Feature learning by inpainting,

    Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, 2016, pp. 2536–2544. 2

    2016

  9. [17]

    Globally and locally consistent image completion,

    Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa, “Globally and locally consistent image completion,”ACM Transactions on Graphics (TOG), vol. 36, no. 4, pp. 1–14,

  10. [18]

    Free-form image inpainting with gated convolution,

    Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang, “Free-form image inpainting with gated convolution,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 4471–4480. 2

    2019

  11. [19]

    Learning joint spatial-temporal transformations for video inpainting,

    Yanhong Zeng, Jianlong Fu, and Hongyang Chao, “Learning joint spatial-temporal transformations for video inpainting,” in European Conference on Computer Vi- sion, 2020, pp. 528–543. 2

    2020

  12. [20]

    Fuseformer: Fusing fine-grained information in transformers for video inpainting,

    Rui Liu, Hanming Deng, Yangyi Huang, Xiaoyu Shi, Lewei Lu, Wenxiu Sun, Xiaogang Wang, Jifeng Dai, and Hongsheng Li, “Fuseformer: Fusing fine-grained information in transformers for video inpainting,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2...

    2021

  13. [21]

    Flow- guided transformer for video inpainting,

    Kaidong Zhang, Jingjing Fu, and Dong Liu, “Flow- guided transformer for video inpainting,” in European Conference on Computer Vision, 2022, pp. 74–90. 2

    2022

  14. [22]

    Youtube-vos: A large-scale video object segmentation benchmark,

    Ning Xu, Linjie Yang, Yuchen Fan, Dingcheng Yue, Yuchen Liang, Jianchao Yang, and Thomas Huang, “Youtube-vos: A large-scale video object segmentation benchmark,” arXiv preprint arXiv:1809.03327, 2018. 3

    2018 arXiv

  15. [23]

    A benchmark dataset and evaluation methodology for video object seg- mentation,

    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 seg- mentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 724–

    2016

  16. [24]

    Image quality assessment: from error vis- ibility to structural similarity,

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli, “Image quality assessment: from error vis- ibility to structural similarity,” IEEE transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004. 3

    2004

  17. [25]

    Video-to-video synthesis,

    Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, and Bryan Catan- zaro, “Video-to-video synthesis,” arXiv preprint arXiv:1808.06601, 2018. 3

    2018 arXiv

  18. [26]

    Quo vadis, action recognition? a new model and the kinetics dataset,

    Joao Carreira and Andrew Zisserman, “Quo vadis, action recognition? a new model and the kinetics dataset,” in proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 6299–6308. 3

    2017

  19. [27]

    5 shows the failure case when outpainting static video

    LIMITATION Fig. 5 shows the failure case when outpainting static video. Our method sometimes blurs (left) or omits (right) the fore- ground that is never seen in a given region. This shows the continuing challenge of static videos in video outpainting. Fig. 5. Failure cases wh...

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Reviewed August 6, 2026 · model on record in the stance chip above.