VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation
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 de...
| Authors | Tarun Gehlaut, Difan Liu, Charu Bansal et al. |
|---|---|
| Primary category | cs.CV |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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