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VectorArk model improves image vectorization with rounded polygons

Researchers have developed VectorArk, a new vision-language model designed for practical image vectorization. Unlike previous models that perform well on synthetic data but struggle with real-world images, VectorArk utilizes a novel rounded polygon representation. This approach simplifies learning and generates visually appealing primitives, while a proposed degradation model enhances robustness against imperfect inputs. Experiments demonstrate VectorArk's superior performance in geometric completeness and artifact suppression across various datasets. AI

IMPACT Introduces a novel approach to image vectorization that improves robustness and visual appeal for real-world applications.

RANK_REASON The cluster contains a research paper detailing a new model for image vectorization. [lever_c_demoted from research: ic=1 ai=1.0]

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tarun Gehlaut, Difan Liu, Charu Bansal, Krutik Malani, Souymodip Chakraborty, Ankit Phogat, Matthew Fisher, Vineet Batra ·

    VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation

    arXiv:2605.24398v1 Announce Type: cross 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 a…