Pith. sign in

REVIEW 1 major objections 27 references

VectorArk's rounded polygon representation and degradation model enable better vectorization of real-world images with superior geometric completeness and fewer artifacts.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · grok-4.3

2026-06-30 13:49 UTC pith:RCDIWUD3

load-bearing objection VectorArk adds a rounded polygon output and degradation model to VLM vectorization for real inputs, but the abstract gives no numbers or comparisons to support the superiority claim. the 1 major comments →

arxiv 2605.24398 v1 pith:RCDIWUD3 submitted 2026-05-23 cs.CV cs.AIcs.GR

VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation

Tarun Gehlaut , Difan Liu , Charu Bansal , Krutik Malani , Souymodip Chakraborty , Ankit Phogat , Matthew Fisher , Vineet Batra This is my paper
classification cs.CV cs.AIcs.GR
keywords image vectorizationrounded polygonsvision-language modelsdegradation modelSVG generationgeometric completenessartifact suppression
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper introduces VectorArk, a VLM-based model for image vectorization that uses a rounded polygon representation to simplify learning and produce smooth primitives. It also includes a degradation model to handle diverse and imperfect inputs like those from text-to-image models. Previous methods perform well only on synthetic benchmarks but fail to generalize to real scenarios. VectorArk shows better results on multiple datasets in terms of geometric completeness and artifact suppression. This matters because practical vectorization needs to work on imperfect real images, not just clean synthetic ones.

Core claim

VectorArk employs a novel rounded polygon representation that simplifies the learning process while naturally producing smooth, visually appealing primitives, and proposes a degradation model that enhances robustness across diverse and imperfect inputs, achieving superior geometric completeness and artifact suppression compared to previous methods across multiple datasets.

What carries the argument

The rounded polygon representation, which simplifies the learning process and produces smooth primitives, combined with the degradation model for robustness.

Load-bearing premise

That the rounded polygon representation simplifies the learning process while naturally producing smooth, visually appealing primitives and that the degradation model enhances robustness across diverse and imperfect inputs.

What would settle it

Evaluating VectorArk on a new set of real-world images with unknown rasterization or from text-to-image models and finding no improvement in geometric completeness or increased artifacts compared to previous methods would falsify the claim.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Improved vectorization for images generated by text-to-image models.
  • Reduced artifacts in vector outputs from unknown rasterization methods.
  • More reliable performance on diverse real-world inputs.
  • Validation through ablations shows each component contributes to the performance gains.

Where Pith is reading between the lines

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

  • Could extend the approach to other vector graphics tasks like editing or animation.
  • The representation might reduce the need for complex post-processing in vectorization pipelines.
  • Testing on even more varied degradation types could further validate robustness.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 0 minor

Summary. The paper introduces VectorArk, a VLM-based model for practical image vectorization. It employs a novel rounded polygon representation that simplifies learning and produces smooth primitives, together with a degradation model for robustness to imperfect real-world inputs. Experiments are said to demonstrate superior geometric completeness and artifact suppression versus prior methods across multiple datasets, with ablations confirming component contributions.

Significance. If the quantitative claims hold, the work would fill a documented gap between synthetic-benchmark performance and real-world generalization for VLM vectorization, potentially benefiting applications that process outputs from text-to-image models or unknown rasterizers.

major comments (1)
  1. [Abstract] Abstract: the central claim of superior geometric completeness and artifact suppression is stated without any metrics, baselines, error analysis, dataset descriptions, or quantitative comparisons, rendering the claim impossible to evaluate from the supplied text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for greater specificity in the abstract. We address the comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim of superior geometric completeness and artifact suppression is stated without any metrics, baselines, error analysis, dataset descriptions, or quantitative comparisons, rendering the claim impossible to evaluate from the supplied text.

    Authors: We agree that the abstract presents the central claim at a high level without numerical support. The full manuscript contains the requested details (quantitative tables, baselines, error analysis, and dataset descriptions) in Sections 4 and 5. To make the abstract self-contained, we will revise it to include a concise statement of the key quantitative gains (e.g., percentage improvements in geometric completeness and artifact metrics versus the strongest baselines on the reported datasets). revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical ML model with no derivational chain

full rationale

The paper presents an empirical VLM-based image vectorization system using a rounded polygon representation and a degradation model. The provided abstract and context contain no equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation chains that reduce claims to inputs by construction. Claims of superior performance rest on experimental results across datasets rather than any algebraic or definitional equivalence. This is the expected outcome for a practical ML architecture paper; the derivation chain is absent, so no circularity can be exhibited.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, training details, or modeling choices from which to extract free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5704 in / 892 out tokens · 48177 ms · 2026-06-30T13:49:32.884683+00:00 · methodology

0 comments
read the original abstract

Recent vision-language model (VLM)-based approaches have achieved impressive results on image vectorization tasks. However, they are typically evaluated on synthetic benchmarks, where clean SVGs are rasterized at high resolution and then re-vectorized. As a result, these methods generalize poorly to real-world scenarios, such as images with unknown rasterization methods or those generated by text-to-image models. We introduce VectorArk, a new VLM-based model designed for robust and practical image vectorization. VectorArk employs a novel rounded polygon representation that simplifies the learning process while naturally producing smooth, visually appealing primitives. We also propose a degradation model that enhances robustness across diverse and imperfect inputs. Our experiments show that, in contrast to previous methods, VectorArk achieves superior geometric completeness and artifact suppression across multiple datasets, with comprehensive ablations validating the contribution of each component.

Figures

Figures reproduced from arXiv: 2605.24398 by Ankit Phogat, Charu Bansal, Difan Liu, Krutik Malani, Matthew Fisher, Souymodip Chakraborty, Tarun Gehlaut, Vineet Batra.

Figure 1
Figure 1. Figure 1: Our framework handles real-world vectorization scenarios, including degraded outputs from text-to-image models and low [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Training and inference pipelines. (a) Training: Both paths are used during training. The upper path converts clean SVGs to rounded polygon ground truth tokens via line-arc fitting. The lower path generates degraded outline rasters through downscaling, rasterization, and classical vectorization. The VLM learns to predict clean geometry from degraded inputs using cross-entropy loss. (b) Test-time scaling: At… view at source ↗
Figure 3
Figure 3. Figure 3: , existing methods exhibit unpredictable behavior when the rasterization pipeline is modified—for instance, switching from one rendering backend to another causes significant degradation in output quality. Similarly, outputs from text-to-image models [8, 14] are increasingly used as inputs for vectorization, yet these generated images often contain distorted shapes and visual characteristics that dif￾fer s… view at source ↗
Figure 4
Figure 4. Figure 4: Rounded polygon representation example. Conver￾sion process from an input SVG to our rounded polygon format. (a) Original source SVG with smooth curves. (b) Line-arc decom￾position where red segments represent lines and green/blue seg￾ments represent circular arcs fitted to the original curves. (c) Cor￾responding polygon vertices (intersection points) before applying corner radii; the final representation … view at source ↗
Figure 5
Figure 5. Figure 5: Rounded polygon vertex computation. Given fitted line-arc primitives, we compute polygon vertices by extending tan￾gents at arc endpoints to their intersection point. for training generative models. To simplify the learning process, we adopt a canonical and compact representation that expresses SVGs using rounded polygons. More specif￾ically, we convert each SVG path into a rounded polygon representation i… view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison on text-to-image generated inputs. training setup identical, while substituting our representa￾tion with OmniSVG’s and StarVector’s representation. For the OmniSVG [24] representation, all SVG commands are restricted to MoveTo (M), LineTo (L), Arc (A), CubicBezier ´ (C), and ClosePath (Z) using only absolute coordinates. We [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Test-time scaling analysis on SVGenius. DinoScore as a function of parallel generation count, averaged over three ran￾dom seeds. Increasing parallel samples improves reconstruction quality through diversity sampling. Ablation on Test-Time Scaling We investigate the ef￾fect of test-time scaling [19] by varying the number of parallel generations and selecting the best output based on DinoScore. For each conf… view at source ↗
Figure 7
Figure 7. Figure 7: Quality comparison with and without degradation model. Four test cases (arranged in 2×2 grid) comparing vectorization quality across different methods. For each input image, we show generated outlines from: Adobe Illustrator (Image Trace), Ours with degradation model, and Ours without degradation model. Adobe Illustrator introduces artifacts and geometric irregularities (highlighted in boxes). Our method w… view at source ↗
Figure 10
Figure 10. Figure 10: Vectorizer comparison and pipeline robustness on a [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Intricate reconstruction example. Blue: original SVG; [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Failure case with dense local path structure concen [PITH_FULL_IMAGE:figures/full_fig_p011_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Easy-category reconstruction: our output (red) versus [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages · 4 internal anchors

  1. [1]

    Adobe Systems Incorporated, San Jose, California, USA, 2025

    Adobe Inc.Adobe Illustrator. Adobe Systems Incorporated, San Jose, California, USA, 2025. Version 29.1 (Creative Cloud). 4

    work page 2025

  2. [2]

    CairoSVG: A Simple SVG Converter based on Cairo, 2012

    Guillaume Ayoub and Kozea. CairoSVG: A Simple SVG Converter based on Cairo, 2012. Maintained by CourtBouil- lon. 2

    work page 2012

  3. [3]

    Sketching clothoid splines using shortest paths.Computer Graphics Forum, 29(2):655–664, 2010

    Ilya Baran, Jaakko Lehtinen, and Jovan Popovi ´c. Sketching clothoid splines using shortest paths.Computer Graphics Forum, 29(2):655–664, 2010. 4

    work page 2010

  4. [4]

    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. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9650–9660, 2021. 5

    work page 2021

  5. [5]

    Image Vec- torization via Gradient Reconstruction.Computer Graphics Forum, 2025

    Souymodip Chakraborty, Vineet Batra, Ankit Phogat, Vish- was Jain, Jaswant Singh Ranawat, Sumit Dhingra, Kevin Wampler, Matthew Fisher, and Michal Luk ´ac. Image Vec- torization via Gradient Reconstruction.Computer Graphics Forum, 2025. 1, 3

    work page 2025

  6. [6]

    Svgenius: Benchmarking llms in svg understand- ing, editing and generation, 2025

    Siqi Chen, Xinyu Dong, Haolei Xu, Xingyu Wu, Fei Tang, Hang Zhang, Yuchen Yan, Linjuan Wu, Wenqi Zhang, Guiyang Hou, Yongliang Shen, Weiming Lu, and Yueting Zhuang. Svgenius: Benchmarking llms in svg understand- ing, editing and generation, 2025. 5, 6

    work page 2025

  7. [7]

    Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks

    Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation mod- els and aligning for generic visual-linguistic tasks. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24185–24198, 2024. 4, 5

    work page 2024

  8. [8]

    Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

    Gemini Team, Google. Gemini 2.5: Pushing the fron- tier with advanced reasoning, multimodality, long context, and next generation agentic capabilities.arXiv preprint arXiv:2507.06261, 2025. 2, 4, 6, 7

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  9. [9]

    Skia: A 2D Graphics Library, 2005

    Google LLC. Skia: A 2D Graphics Library, 2005. An open source 2D graphics library sponsored and managed by Google. 2

    work page 2005

  10. [10]

    LoRA: Low-Rank Adaptation of Large Language Models

    Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 5

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  11. [11]

    Differentiable vector graphics rasterization for edit- ing and learning.ACM Transactions on Graphics (TOG), 39 (6):1–15, 2020

    Tzu-Mao Li, Michal Luk ´aˇc, Micha¨el Gharbi, and Jonathan T Barron. Differentiable vector graphics rasterization for edit- ing and learning.ACM Transactions on Graphics (TOG), 39 (6):1–15, 2020. 3

    work page 2020

  12. [12]

    Decoupled weight de- cay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight de- cay regularization. InInternational Conference on Learning Representations, 2019. 5

    work page 2019

  13. [13]

    Towards layer- wise image vectorization

    Xu Ma, Yuqian Zhou, Xingqian Xu, Bin Sun, Valerii Filev, Nikita Orlov, Yun Fu, and Humphrey Shi. Towards layer- wise image vectorization. InProceedings of the IEEE con- ference on computer vision and pattern recognition, 2022. 1, 3

    work page 2022

  14. [14]

    Gpt-4o system card

    OpenAI. Gpt-4o system card. Technical report, OpenAI,

    work page

  15. [15]

    VTracer: Raster to Vector Graphics Converter, 2020

    Sanford Pun and Chris Tsang. VTracer: Raster to Vector Graphics Converter, 2020. Vision Cortex documentation. 4, 1

    work page 2020

  16. [16]

    Juan A. Rodriguez, Haotian Zhang, Abhay Puri, Aarash Feizi, Rishav Pramanik, Pascal Wichmann, Arnab Mondal, Mohammad Reza Samsami, Rabiul Awal, Perouz Taslakian, Spandana Gella, Sai Rajeswar, David Vazquez, Christopher Pal, and Marco Pedersoli. Rendering-aware reinforcement learning for vector graphics generation, 2025. 3

    work page 2025

  17. [17]

    Starvector: Generating scal- able vector graphics code from images.arXiv preprint arXiv:2312.11556, 2025

    Juan A Rodriguez et al. Starvector: Generating scal- able vector graphics code from images.arXiv preprint arXiv:2312.11556, 2025. 1, 2, 3, 5, 6, 7

    work page arXiv 2025

  18. [18]

    Potrace: a polygon-based tracing algorithm

    Peter Selinger. Potrace: a polygon-based tracing algorithm. http://potrace.sourceforge.net/, 2003. 1, 3

    work page 2003

  19. [19]

    Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

    Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Ku- mar. Scaling llm test-time compute optimally can be more effective than scaling model parameters. arXiv preprint arXiv:2408.03314, 2024. 7

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  20. [20]

    Internsvg: Towards unified svg tasks with multimodal large language models

    Haomin Wang, Jinhui Yin, Qi Wei, Wenguang Zeng, Lixin Gu, Shenglong Ye, Zhangwei Gao, Yaohui Wang, Yanting Zhang, Yuanqi Li, et al. Internsvg: Towards unified svg tasks with multimodal large language models.arXiv preprint arXiv:2510.11341, 2025. 5

    work page arXiv 2025

  21. [21]

    Image quality assessment: From error visibility to structural similarity.IEEE Transactions on Image Process- ing, 13(4):600–612, 2004

    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 Process- ing, 13(4):600–612, 2004. 5

    work page 2004

  22. [22]

    Empowering llms to under- stand and generate complex vector graphics.arXiv preprint arXiv:2412.11102, 2024

    Ximing Xing, Juncheng Hu, Guotao Liang, Jing Zhang, Dong Xu, and Qian Yu. Empowering llms to under- stand and generate complex vector graphics.arXiv preprint arXiv:2412.11102, 2024. 1, 3

    work page arXiv 2024

  23. [23]

    Effective Clipart Image Vectorization Through Direct Optimization of Bezigons

    Ming Yang, Hongyang Chao, Chi Zhang, Jun Guo, Lu Yuan, and Jian Sun. Effective clipart image vectorization through direct optimization of bezigons. InarXiv preprint arXiv:1602.01913, 2016. Available online. 3

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  24. [24]

    Omnisvg: A unified scalable vector graphics genera- tion model.arXiv preprint arxiv:2504.06263, 2025

    Yiying Yang, Wei Cheng, Sijin Chen, Xianfang Zeng, Ji- axu Zhang, Liao Wang, Gang Yu, Xinjun Ma, and Yu-Gang Jiang. Omnisvg: A unified scalable vector graphics genera- tion model.arXiv preprint arxiv:2504.06263, 2025. 1, 3, 5, 6

    work page arXiv 2025

  25. [25]

    The unreasonable effectiveness of deep features as a perceptual metric

    Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shecht- man, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. InProceedings of the IEEE Conference on Computer Vision and Pattern Recogni- tion (CVPR), pages 586–595, 2018. 5 VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation Supple...

    work page 2018

  26. [26]

    Outline Comparison on Noisy Inputs Figure 9 compares the generated outlines on noisy inputs

    Additional Results 6.1. Outline Comparison on Noisy Inputs Figure 9 compares the generated outlines on noisy inputs. Our method maintains cleaner geometric structure com- pared to competing approaches. Noisy Input Raster Adobe Illustrator ( Image Trace ) VectorArk StarVector 8B OmniSVG x Figure 9. Outline comparison across methods on noisy inputs. (Zoom i...

    work page arXiv

  27. [27]

    This design simplifies the learning task and improves robustness to appearance variations

    Post-Processing: Color and Stroke Recovery As described in the main paper, our model predicts colorless geometry from outline-based inputs. This design simplifies the learning task and improves robustness to appearance variations. Practical vectorization, however, also requires recovering colors and stroke properties from the original input image. We pres...

    work page arXiv 1944