Text-Guided Vector Graphics Customization

• URL: http://arxiv.org/abs/2309.12302v1
• Date: Thu, 21 Sep 2023 17:59:01 GMT
• Title: Text-Guided Vector Graphics Customization
• Authors: Peiying Zhang, Nanxuan Zhao, Jing Liao
• Abstract summary: We propose a novel pipeline that generates high-quality customized vector graphics based on textual prompts. Our method harnesses the capabilities of large pre-trained text-to-image models. We evaluate our method using multiple metrics from vector-level, image-level and text-level perspectives.
• Score: 31.41266632288932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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