VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918519504896000 |
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| author | Gehlaut, Tarun Liu, Difan Bansal, Charu Malani, Krutik Chakraborty, Souymodip Phogat, Ankit Fisher, Matthew Batra, Vineet |
| author_facet | Gehlaut, Tarun Liu, Difan Bansal, Charu Malani, Krutik Chakraborty, Souymodip Phogat, Ankit Fisher, Matthew Batra, Vineet |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_24398 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation Gehlaut, Tarun Liu, Difan Bansal, Charu Malani, Krutik Chakraborty, Souymodip Phogat, Ankit Fisher, Matthew Batra, Vineet Computer Vision and Pattern Recognition Artificial Intelligence Graphics 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. |
| title | VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics |
| url | https://arxiv.org/abs/2605.24398 |