VectorArk：以圆角多边形表示学习实用的图像矢量化
VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation
圆角多边形表示把矢量图元的学习简化为可回归的几何参数，配合退化模型提升对未知栅格化方式与生成图像的鲁棒性，可复用于任何需要从像素回归结构化几何表示的视觉任务。
原文依据 VectorArk employs a novel rounded polygon representation that simplifies the learning process while naturally producing smooth, visually appealing primitives.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论近期基于视觉语言模型（VLM）的方法在图像矢量化任务上取得了令人印象深刻的结果。然而，这些方法通常在合成基准上评估，即把干净的 SVG 以高分辨率栅格化后再重新矢量化。因此，这些方法对真实场景的泛化能力较差，例如栅格化方式未知的图像或由文本到图像模型生成的图像。我们提出 VectorArk，一种新的基于 VLM 的模型，面向鲁棒且实用的图像矢量化。VectorArk 采用一种新颖的圆角多边形表示，简化了学习过程，同时自然产生平滑、视觉上吸引人的图元。我们还提出一种退化模型，以增强在多样且不完美输入下的鲁棒性。实验表明，与先前方法不同，VectorArk 在多个数据集上实现了更优的几何完整性与伪影抑制，全面的消融实验验证了各组件的贡献。
英文原文摘要 1010 字符
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.