Vector graphics are widely used in digital art and valued by designers for their scalability and layer-wise topological properties. However, the creation and editing of vector graphics necessitate creativity and design expertise, leading to a time-consuming process. In this paper, we propose a novel pipeline that generates high-quality customized vector graphics based on textual prompts while preserving the properties and layer-wise information of a given exemplar SVG. Our method harnesses the capabilities of large pre-trained text-to-image models. By fine-tuning the cross-attention layers of the model, we generate customized raster images guided by textual prompts. To initialize the SVG, we introduce a semantic-based path alignment method that preserves and transforms crucial paths from the exemplar SVG. Additionally, we optimize path parameters using both image-level and vector-level losses, ensuring smooth shape deformation while aligning with the customized raster image. We extensively evaluate our method using multiple metrics from vector-level, image-level, and text-level perspectives. The evaluation results demonstrate the effectiveness of our pipeline in generating diverse customizations of vector graphics with exceptional quality. The project page is https://intchous.github.io/SVGCustomization.

翻译：矢量图形广泛应用于数字艺术，因其可缩放性和逐层拓扑特性而受到设计师的重视。然而，矢量图形的创建与编辑需要创造力和设计专业知识，导致过程耗时费力。本文提出了一种新型流水线，能够基于文本提示生成高质量定制的矢量图形，同时保留给定示例SVG的属性和分层信息。我们的方法利用了大规模预训练文本到图像模型的能力。通过微调模型的交叉注意力层，我们生成受文本提示引导的定制光栅图像。为初始化SVG，我们引入了一种基于语义的路径对齐方法，该方法保留并转换示例SVG中的关键路径。此外，我们使用图像级和矢量级损失函数优化路径参数，确保在平滑形变的同时与定制光栅图像对齐。我们从矢量级、图像级和文本级等多个指标全面评估了该方法。评估结果表明，我们的流水线能够生成高质量、多样化的矢量图形定制结果。项目页面为https://intchous.github.io/SVGCustomization。