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
this https URL

提出了一种基于文本提示生成高质量定制化矢量图形的新型流程，该流程利用大型预训练的文本到图像模型的能力，在保留给定示例 SVG 的属性和分层信息的同时生成定制化的光栅图像，方法中引入了基于语义的路径对齐方法以保留和转换关键路径，使用图像级和矢量级损失优化路径参数以确保平滑的形状变形与定制化的光栅图像对齐，通过从矢量级、图像级和文本级多个角度对其进行评估，评估结果表明该流程在生成具有优秀质量的矢量图形定制化方面具有有效性。