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REVIEW 4 major objections 6 minor 1 cited by

LinkTo-Anime: A 2D Animation Optical Flow Dataset from 3D Model Rendering

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Anime-specific flow dataset lifts accuracy across all tested models.

desk verdict A genuinely useful cel-anime optical flow dataset, but the real-production transfer claim is asserted rather than demonstrated. read the letter →

arxiv 2506.02733 v2 pith:MB7U6TCH submitted 2025-06-03 cs.CV cs.AI

Xiaoyi Feng , Kaifeng Zou , Caichun Cen , Tao Huang , Hui Guo , Zizhou Huang , Yingli Zhao , Mingqing Zhang
show 4 more authors
Ziyuan Zheng Diwei Wang Yuntao Zou Dagang Li
This is my paper
classification cs.CVcs.AI
keywords opticalflowcelanimationanimedataset3Drenderinglineartbenchmarkframeinterpolationcolorization
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 introduces LinkTo-Anime, a dataset of 395 rendered video clips built to look like cel-anime production frames, with dense forward and backward optical flow, occlusion masks, line-art versions, and skeletal motion data. Its central claim is that this is the first high-quality optical flow dataset tailored to cel anime character motion, and that fine-tuning standard optical flow networks on it improves their endpoint error on every reported metric. If true, the dataset fills a gap left by existing synthetic and animation datasets, which either lack detail or do not match the white-background, stationary-camera style of real production cuts. A sympathetic reader would care because optical flow underpins tasks like anime line colorization and frame interpolation, and a domain-specific benchmark could make those tools more reliable.

What carries the argument

The pipeline renders pre-processed 3D models with a cel-style appearance: Lambertian diffuse shading plus a normal map for light-dark contrast, and contour lines produced with the Cartoon Line Creator plugin refined by a weld modifier, avoiding the FLAT rendering used by AnimeRun to preserve fine details such as eye interior and collar structure. Ground-truth optical flow comes from Blender's vector motion, giving per-pixel forward and backward correspondence, and occlusions are derived from those maps. Fixed camera settings per clip match the reutilized-background convention of cel production.

What would settle it

Collect a held-out set of production cel frames from several independent studios, obtain manual or semi-automatic optical flow annotations on a subset, and compare fine-tuning on LinkTo-Anime versus AnimeRun and CreativeFlow+ on those frames; if LinkTo-Anime no longer yields lower EPE or better downstream colorization/interpolation metrics, the central claim fails.

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Extended reading notes

Core claim

LinkTo-Anime provides 29,270 frames across 395 clips at 1440×2560 resolution, rendered from 80 open-source VRoid models that were manually refined, rigged with Mixamo skeletons, and animated with motions such as boxing and dancing, each clip shot from five fixed viewpoints that mimic typical cuts. The authors claim that fine-tuning PWC-Net, RAFT, GMA, and GMFlow on this dataset consistently improves EPE across background, foreground, occluded, and speed-stratified regions compared with fine-tuning on Sintel, CreativeFlow+, or AnimeRun, and that substituting the fine-tuned RAFT into BasicPBC colorization and AnimeInterp interpolation improves their metrics. The paper also demonstrates via PCA, t-SNE, and KL divergence of luminance histograms that LinkTo-Anime's distribution is closer to a private 1,100-frame 'Cel' set of real production files than the other public datasets.

Load-bearing premise

The claim that LinkTo-Anime matches real cel production imagery rests on similarity to a single private 1,100-frame Cel set; if that set does not represent the broader anime industry, or if PCA/t-SNE and luminance KL divergence are insufficient evidence of style match, the dataset's real-world value is not established.

Editorial extensions

If this is right

  • Optical flow models fine-tuned on LinkTo-Anime generalize to the private Cel dataset, suggesting flow learned from these rendered frames transfers to real anime production files.
  • Substituting LinkTo-Anime-fine-tuned RAFT raises BasicPBC colorization accuracy to 82.94% and background MIoU to 60.82%, improving on fine-tuning with Sintel or AnimeRun.
  • AnimeInterp frame interpolation on Cel frames reaches higher PSNR (19.63) and SSIM (0.884) when its RAFT module is fine-tuned on LinkTo-Anime.
  • The dataset's white backgrounds and stationary camera setup make it a closer benchmark for production cuts than full-scene animation datasets, and models fine-tuned on it produce cleaner backgrounds and more accurate foregrounds.

Reading between the lines

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

  • The paper's distribution evidence is limited to a 1,100-frame private set; an independent evaluation against production files from other studios would settle whether the style match holds beyond that sample.
  • Because all clips come from 80 VRoid models with Mixamo motions, the dataset may underrepresent flat-color, low-detail or hand-drawn styles common in older cel animation; the benchmark's advantage might shrink on such imagery.
  • The rendering choices—Lambertian shading, normal-map details, and welded line art—set a new design point that future synthetic anime datasets could adopt, but the paper does not ablate each choice's contribution to the downstream gains.
  • The claimed supremacy might be specific to the four tested architectures; other flow networks or transformer-based models could respond differently to this domain shift.
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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

4 major / 6 minor

Summary. The paper introduces LinkTo-Anime, a synthetic optical flow dataset for cel (celluloid) animation, generated by rendering 80 open-source VRoid models with Mixamo-driven motions. The dataset contains 395 video clips, roughly 29,000 frames at 1440x2560 resolution, split into train/validation/test, with forward and backward optical flow, occlusion masks, rendered color frames, and line-art versions. The authors benchmark PWC-Net, RAFT, GMA, and GMFlow under four fine-tuning protocols (Sintel, CreativeFlow+, AnimeRun, and LinkTo-Anime), report endpoint-error metrics on the LinkTo-Anime test set, and evaluate two downstream applications: line-drawing colorization with BasicPBC and frame interpolation with AnimeInterp. They also introduce a private 1,100-frame 'Cel' dataset of real production files used for distribution analysis and qualitative validation. The central claim is that LinkTo-Anime is the first high-quality optical flow dataset tailored to cel animation production and that fine-tuning on it consistently improves optical flow prediction.

Significance. If the claims hold, LinkTo-Anime is a genuinely useful community resource: it is large-scale, high-resolution, and provides exact pixel-level flow labels and occlusion masks together with line-art renderings, which are scarce for the cel-animation domain. The benchmark across four optical flow methods and two downstream tasks is a solid starting point, and the public release on Hugging Face is a concrete strength. However, the strongest claims about generalization to real production files are not yet empirically pinned down, because the main quantitative evaluation is performed on the same rendering pipeline used to create the dataset, and the only cross-domain validation relies on a private, unreleased Cel set with sparse manual annotations and no error bars. The dataset contribution itself is valuable even if the production-transfer claim is later scoped down, but the paper as written overstates what the evidence supports.

major comments (4)
  1. [Abstract; §4.2, Table 2] The claim that fine-tuning on LinkTo-Anime 'consistently improves optical flow prediction performance for all EPE metrics' is demonstrated only on the LinkTo-Anime test set, which is generated by the same 3D rendering pipeline described in Secs. 3.1–3.2. This is an in-distribution evaluation: a model fine-tuned on LinkTo-Anime is tested on frames drawn from the same synthetic distribution. The abstract and conclusion should either scope this claim to the proposed benchmark or provide a cross-domain evaluation on an independently released real-production set.
  2. [§3.3; §4.2; Table 3b] The evidence that LinkTo-Anime transfers to real cel production files relies on PCA/t-SNE visual overlap, luminance KL divergence, qualitative flow visualizations, and a private 1,100-frame Cel dataset that is not released. The downstream interpolation improvement on Cel (+0.47 PSNR, +0.002 SSIM) is reported without error bars or significance tests, and the manual keypoints used for evaluation are described only as 'coarse ground truth' in Sec. 4.2. To substantiate the production-generalization claim, the authors should release Cel (or a curated annotated subset) and report confidence intervals or paired significance tests.
  3. [§4.1] The CreativeFlow+ baseline is not controlled under the paper's own training protocol. The authors state that because the CreativeFlow+ download link is unavailable, they used pretrained weights provided in AnimeRun, and they omit RAFT ft. T+Cr entirely. This makes the 'second-best' comparisons in Table 2 and the conclusions drawn from them in Sec. 4.2 unreliable: the Cr columns may come from models trained with different data schedules or iteration counts. The authors should retrain all baselines under their own protocol or clearly label the Cr results as inherited from a different setup.
  4. [Table 2] The benchmark reports a single run per model-dataset combination with no variance estimates. Given that the differences between the top methods can be as small as 0.10 EPE (GMA ft. T+LT versus PWC-Net ft. T+LT), the claimed ranking and the 'consistently improves' language are not robust without repeated runs or error bars. At minimum, the authors should report the number of runs and the observed variability for the key comparisons.
minor comments (6)
  1. [§3.1; §4.2; Fig. 4] There are several typos: 'the very frist step', 'libe art output', 'Aadapting models' in Fig. 4, and 'his is likely' in Sec. 4.2. The manuscript needs a careful proofread.
  2. [References] References [30] and [31] are the same paper (Deep Geometrized Cartoon Line Inbetweening); one should be removed.
  3. [Table 3] The caption 'Qualitative result of two Animation Application' is inaccurate because both panels are quantitative tables; please change it to 'Quantitative results'.
  4. [Figure 8] The caption reads '(a) Anime Interpolation (b) Anime Interpolation'; the first panel is line-drawing colorization and the second is frame interpolation, so the caption should distinguish them.
  5. [§2.2] The citation 'Buter et al.' should be 'Butler et al.' for the MPI-Sintel reference.
  6. [§3.3] The sentence 'the flow directions ... are mostly concentrated around 0, 90, -90, and 180 degree;' has a typographical issue with the semicolon and 'degree'; please polish.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the dataset, flow labels, and benchmark are self-contained, with limitations that are empirical validity concerns rather than circular reasoning.

full rationale

The paper's central deliverable is a rendered optical flow dataset whose labels come from Blender's vector motion, not from any model prediction or fitted parameter. Fine-tuning and evaluation use disjoint splits of LinkTo-Anime, and comparisons are also made against external datasets (Sintel, AnimeRun, CreativeFlow+) and downstream methods (BasicPBC, AnimeInterp). The private Cel set is used only as an additional validation target, and the paper explicitly states its ground truth is 'coarse' and that it 'primarily relied on visual comparisons' (Sec. 4.2); this is a stated limitation on evidence strength, not a circular derivation. The borrowed CreativeFlow+ checkpoints from AnimeRun (Sec. 4.1) weaken the baseline control, but again this is an experimental-rigor issue, not a case of a prediction being equivalent to its input. No load-bearing step reduces by the paper's own equations or by self-citation to its own inputs, and no self-citations are used as justification. Thus the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on assumptions about representativeness and annotation accuracy rather than on derived equations. No free parameters are fitted; the dataset design choices and benchmark hyperparameters are the main assumptions.

assumptions (4)
  • domain assumption Rendered 3D models with cel shading and Mixamo motions are representative of real cel animation production images.
    Sec. 3.3 validates this only by PCA, t-SNE, and luminance KL divergence against a private Cel set; no independent confirmation.
  • domain assumption Blender vector motion output gives ground-truth 2D optical flow.
    Assumed in Sec. 3.1 and Fig. 1; no validation of flow accuracy at occlusion boundaries.
  • domain assumption Occlusion masks derived during rendering are accurate.
    Mentioned in Sec. 1 and 3, but the generation method is not specified.
  • domain assumption The fine-tuning protocol and data split (1:10 FlyingThings3D, 40k iterations) are fair for comparing datasets.
    Sec. 4.1 follows AnimeRun, but the CreativeFlow+ baseline uses pretrained weights, not a fresh fine-tune.

how reviews work

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Cite this review

Pith. "Pith review of LinkTo-Anime: A 2D Animation Optical Flow Dataset from 3D Model Rendering." pith.science (2026). https://pith.science/paper/MB7U6TCH

@misc{pith2026250602733,
  author       = {Pith},
  title        = {Pith review of: LinkTo-Anime: A 2D Animation Optical Flow Dataset from 3D Model Rendering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MB7U6TCH}},
  note         = {Machine review of arXiv:2506.02733}
}
read the original abstract

Existing optical flow datasets focus primarily on real-world simulation or synthetic human motion, but few are tailored to Celluloid(cel) anime character motion: a domain with unique visual and motion characteristics. To bridge this gap and facilitate research in optical flow estimation and downstream tasks such as anime video generation and line drawing colorization, we introduce LinkTo-Anime, the first high-quality dataset specifically designed for cel anime character motion generated with 3D model rendering. LinkTo-Anime provides rich annotations including forward and backward optical flow, occlusion masks, and Mixamo Skeleton. The dataset comprises 395 video sequences, totally 24,230 training frames, 720 validation frames, and 4,320 test frames. Furthermore, a comprehensive benchmark is constructed with various optical flow estimation methods to analyze the shortcomings and limitations across multiple datasets.

Figures

Figures reproduced from arXiv: 2506.02733 by the authors.

Figure 1
Figure 1. We construct a dataset named LinkTo-Anime by augmenting open-source 3D models with manual model refinement, shading. We animate them using Mixamo skeletons to generate diverse character motions. The frames are rendered from multiple viewpoints, the resulting colored and line drawing resemble two styles in 2D cel animation production. Also, we provide pixel-level correspondence as the ground truth for optical flow es… view at source ↗
Figure 2
Figure 2. Illustration of the compositing process in cel animation. During production, different layers, such as characters and props are first [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Comparative visualization of images and optical [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Overview of our data preparation and rendering pipeline. The process consists of five stages: (1) Acquiring 3D models from open-source platforms (e.g., Vroid); (2) Preprocessing models to clean and restructure them; (3) Rigging and generating motions using Mixamo; (4) …
Figure 5
Figure 5. Figure 5: Visualization of two rendering pipelines. Each row rep [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 6
Figure 6. Figure 6: Comparison on dataset distributions. (a) PCA: LinkTo [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Visualization of optical flow predictions on the LinkTo-Anime test set (a) and Cel-dataset (b). In (a), the model fine-tuned on LinkTo-Anime demonstrates better performance compared to those fine-tuned on other datasets such as Sintel [1], CreativeFlow+ [27], and Anime…
Figure 8
Figure 8. Figure 8: Visualization of two anime applications: (a) Anime In [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

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Forward citations

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Reference graph

Works this paper leans on

46 extracted references · 42 canonical work pages · cited by 1 Pith paper

  1. [1]

    D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black. A naturalistic open source movie for optical flow evaluation. In European Conf. on Computer Vision (ECCV), pages 611–

  2. [2]

    The animation transformer: Visual correspondence via seg- ment matching, 2021

    Evan Casey, V ´ıctor P´erez, Zhuoru Li, Harry Teitelman, Nick Boyajian, Tim Pulver, Mike Manh, and William Grisaitis. The animation transformer: Visual correspondence via seg- ment matching, 2021. 2, 3

    work page 2021

  3. [3]

    Panic-3d: Stylized single- view 3d reconstruction from portraits of anime characters

    Shuhong Chen, Kevin Zhang, Yichun Shi, Heng Wang, Yi- heng Zhu, Guoxian Song, Sizhe An, Janus Kristjansson, Xiao Yang, and Matthias Zwicker. Panic-3d: Stylized single- view 3d reconstruction from portraits of anime characters. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition (CVPR), 2023. 5

    work page 2023

  4. [4]

    Paint bucket coloriza- tion using anime character color design sheets, 2024

    Yuekun Dai, Qinyue Li, Shangchen Zhou, Yihang Luo, Chongyi Li, and Chen Change Loy. Paint bucket coloriza- tion using anime character color design sheets, 2024. 2, 3, 4, 8

    work page 2024

  5. [5]

    Learning inclusion matching for anima- tion paint bucket colorization, 2024

    Yuekun Dai, Shangchen Zhou, Qinyue Li, Chongyi Li, and Chen Change Loy. Learning inclusion matching for anima- tion paint bucket colorization, 2024. 2, 3, 7, 8

    work page 2024

  6. [6]

    Memflow: Optical flow es- timation and prediction with memory

    Qiaole Dong and Yanwei Fu. Memflow: Optical flow es- timation and prediction with memory. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19068–19078, 2024. 3

    work page 2024

  7. [7]

    Virtual worlds as proxy for multi-object tracking anal- ysis

    Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig. Virtual worlds as proxy for multi-object tracking anal- ysis. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4340–4349, 2016. 3

    work page 2016

  8. [8]

    Flowformer: A transformer architecture for optical flow

    Zhaoyang Huang, Xiaoyu Shi, Chao Zhang, Qiang Wang, Ka Chun Cheung, Hongwei Qin, Jifeng Dai, and Hongsheng Li. Flowformer: A transformer architecture for optical flow. In European conference on computer vision, pages 668–685. Springer, 2022. 3

    work page 2022

Show all 46 references
  1. [9]

    Real-time intermediate flow estimation for video frame interpolation

    Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou. Real-time intermediate flow estimation for video frame interpolation. In European Conference on Com- puter Vision, pages 624–642. Springer, 2022. 3

    2022

  2. [10]

    Lvcd: Reference-based lineart video colorization with diffusion models

    Zhitong Huang, Mohan Zhang, and Jing Liao. Lvcd: Reference-based lineart video colorization with diffusion models. ACM Transactions on Graphics, 43(6):1–11, 2024. 2, 3

    2024

  3. [11]

    Distractflow: Improving optical flow estimation via real- istic distractions and pseudo-labeling

    Jisoo Jeong, Hong Cai, Risheek Garrepalli, and Fatih Porikli. Distractflow: Improving optical flow estimation via real- istic distractions and pseudo-labeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13691–13700, 2023. 3

    2023

  4. [12]

    Learning to estimate hidden motions with global motion aggregation

    Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, and Richard Hartley. Learning to estimate hidden motions with global motion aggregation. In Proceedings of the IEEE/CVF international conference on computer vision , pages 9772– 9781, 2021. 3, 6

    2021

  5. [13]

    A unified pyramid recurrent net- work for video frame interpolation

    Xin Jin, Longhai Wu, Jie Chen, Youxin Chen, Jayoon Koo, and Cheul-hee Hahm. A unified pyramid recurrent net- work for video frame interpolation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1578–1587, 2023. 3

    2023

  6. [14]

    Anim- director: A large multimodal model powered agent for con- trollable animation video generation

    Yunxin Li, Haoyuan Shi, Baotian Hu, Longyue Wang, Ji- ashun Zhu, Jinyi Xu, Zhen Zhao, and Min Zhang. Anim- director: A large multimodal model powered agent for con- trollable animation video generation. In SIGGRAPH Asia 2024 Conference Papers, pages 1–11, 2024. 3

    2024

  7. [15]

    Manganinja: Line art colorization with precise reference following, 2025

    Zhiheng Liu, Ka Leong Cheng, Xi Chen, Jie Xiao, Hao Ouyang, Kai Zhu, Yu Liu, Yujun Shen, Qifeng Chen, and Ping Luo. Manganinja: Line art colorization with precise reference following, 2025. 2, 3

    2025

  8. [16]

    Sketchbetween: Video-to-video synthesis for sprite animation via sketches

    Dagmar Loftsdottir and Matthew Guzdial. Sketchbetween: Video-to-video synthesis for sprite animation via sketches. In Proceedings of the 17th International Conference on the Foundations of Digital Games, pages 1–7, 2022. 3

    2022

  9. [17]

    A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation

    Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and ...

    2016

  10. [18]

    Optical flow based line drawing frame interpolation using distance transform to support inbetweenings

    Rei Narita, Keigo Hirakawa, and Kiyoharu Aizawa. Optical flow based line drawing frame interpolation using distance transform to support inbetweenings. In 2019 IEEE Interna- tional Conference on Image Processing (ICIP), pages 4200–

    2019

  11. [19]

    Conditional image-to-video gener- ation with latent flow diffusion models

    Haomiao Ni, Changhao Shi, Kai Li, Sharon X Huang, and Martin Renqiang Min. Conditional image-to-video gener- ation with latent flow diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18444–18455, 2023. 3

    2023

  12. [20]

    Softmax splatting for video frame interpolation

    Simon Niklaus and Feng Liu. Softmax splatting for video frame interpolation. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 5437–5446, 2020. 3

    2020

  13. [21]

    Cel animation cut, 2023

    Online. Cel animation cut, 2023. https://brush. ninja/glossary/animation/cut/. 2

    2023

  14. [22]

    Adobe flash, 2025

    Online. Adobe flash, 2025. https://zh.wikipedia. org/wiki/Adobe_Flash. 2

    2025

  15. [23]

    Clip studio paint, 2025

    Online. Clip studio paint, 2025. https : / / en . wikipedia.org/wiki/Clip_Studio_Paint. 2

    2025

  16. [24]

    Toonz, 2025

    Online. Toonz, 2025. https://en.wikipedia.org/ wiki/Toonz. 2

    2025

  17. [25]

    Flowformer++: Masked cost volume autoen- coding for pretraining optical flow estimation

    Xiaoyu Shi, Zhaoyang Huang, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, and Hongsheng Li. Flowformer++: Masked cost volume autoen- coding for pretraining optical flow estimation. In Proceed- ings of the IEEE/CVF conference on computer vision a...

    2023

  18. [26]

    A tutorial on principal component analysis,

    Jonathon Shlens. A tutorial on principal component analysis,

  19. [27]

    Creative flow+ dataset

    Maria Shugrina, Ziheng Liang, Amlan Kar, Jiaman Li, An- gad Singh, Karan Singh, and Sanja Fidler. Creative flow+ dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 5384– 5393, 2019. 2, 3, 6, 7, 8

    2019

  20. [28]

    Metaxas, Chen Change Loy, and Ziwei Liu

    Li Siyao, Shiyu Zhao, Weijiang Yu, Wenxiu Sun, Dimitris N. Metaxas, Chen Change Loy, and Ziwei Liu. Deep animation video interpolation in the wild, 2021. 7, 8, 9

    2021

  21. [29]

    Animerun: 2d animation visual corre- spondence from open source 3d movies, 2022

    Li Siyao, Yuhang Li, Bo Li, Chao Dong, Ziwei Liu, and Chen Change Loy. Animerun: 2d animation visual corre- spondence from open source 3d movies, 2022. 2, 3, 4, 5, 6, 7, 8, 9

    2022

  22. [30]

    Deep geometrized cartoon line inbe- tweening

    Li Siyao, Tianpei Gu, Weiye Xiao, Henghui Ding, Ziwei Liu, and Chen Change Loy. Deep geometrized cartoon line inbe- tweening. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 7291–7300,

  23. [31]

    Deep geometrized cartoon line inbe- tweening

    Li Siyao, Tianpei Gu, Weiye Xiao, Henghui Ding, Ziwei Liu, and Chen Change Loy. Deep geometrized cartoon line inbe- tweening. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7291–7300, 2023. 3

    2023

  24. [32]

    Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume

    Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8934–8943,

  25. [33]

    Disentan- gling architecture and training for optical flow

    Deqing Sun, Charles Herrmann, Fitsum Reda, Michael Ru- binstein, David J Fleet, and William T Freeman. Disentan- gling architecture and training for optical flow. In European Conference on Computer Vision , pages 165–182. Springer,

  26. [34]

    Generative ai for cel-animation: A survey, 2025

    Yunlong Tang, Junjia Guo, Pinxin Liu, Zhiyuan Wang, Hang Hua, Jia-Xing Zhong, Yunzhong Xiao, Chao Huang, Luchuan Song, Susan Liang, Yizhi Song, Liu He, Jing Bi, Mingqian Feng, Xinyang Li, Zeliang Zhang, and Chenliang Xu. Generative ai for cel-animation: A survey, 2025. 2, 3

    2025

  27. [35]

    Raft: Recurrent all-pairs field transforms for optical flow, 2020

    Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow, 2020. 3, 6, 9

    2020

  28. [36]

    Visualizing data using t-sne

    Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research , 9 (11), 2008. 6

    2008

  29. [37]

    Coloring anime line art videos with transformation region enhance- ment network

    Ning Wang, Muyao Niu, Zhi Dou, Zhihui Wang, Zhiyong Wang, Zhaoyan Ming, Bin Liu, and Haojie Li. Coloring anime line art videos with transformation region enhance- ment network. Pattern Recognition, 141:109562, 2023. 3

    2023

  30. [38]

    Image quality assessment: from error visibility to structural similarity

    Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Si- moncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600–612, 2004. 8

    2004

  31. [39]

    Tooncrafter: Generative cartoon interpolation, 2024

    Jinbo Xing, Hanyuan Liu, Menghan Xia, Yong Zhang, Xin- tao Wang, Ying Shan, and Tien-Tsin Wong. Tooncrafter: Generative cartoon interpolation, 2024. 2, 3

    2024

  32. [40]

    Gmflow: Learning optical flow via global matching

    Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8121–8130,

  33. [41]

    Quadratic video interpolation

    Xiangyu Xu, Li Siyao, Wenxiu Sun, Qian Yin, and Ming- Hsuan Yang. Quadratic video interpolation. Advances in Neural Information Processing Systems, 32, 2019. 3

    2019

  34. [42]

    User-guided line art flat fill- ing with split filling mechanism

    Lvmin Zhang, Chengze Li, Edgar Simo-Serra, Yi Ji, Tien- Tsin Wong, and Chunping Liu. User-guided line art flat fill- ing with split filling mechanism. In 2021 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) , pages 9884–9893, 2021. 3

    2021

  35. [43]

    Maskflownet: Asymmetric feature matching with learnable occlusion mask

    Shengyu Zhao, Yilun Sheng, Yue Dong, Eric I Chang, Yan Xu, et al. Maskflownet: Asymmetric feature matching with learnable occlusion mask. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 6278–6287, 2020. 3

    2020

  36. [44]

    Cogcartoon: towards practi- cal story visualization

    Zhongyang Zhu and Jie Tang. Cogcartoon: towards practi- cal story visualization. International Journal of Computer Vision, 133(4):1808–1833, 2025. 3

    2025

  37. [45]

    Colorflow: Retrieval- augmented image sequence colorization, 2024

    Junhao Zhuang, Xuan Ju, Zhaoyang Zhang, Yong Liu, Shiyi Zhang, Chun Yuan, and Ying Shan. Colorflow: Retrieval- augmented image sequence colorization, 2024. 3

    2024

  38. [625]

    3, 6, 7, 8

    Springer-Verlag, 2012. 3, 6, 7, 8

    2012

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.