SVGDreamer: Text Guided SVG Generation with Diffusion Model

Understand

Text-guided scalable vector graphics (SVG) synthesis has broad applications in icon and sketch generation.

• However, existing text-to-SVG methods often suffer from limited editability, suboptimal visual quality, and low sample diversity.
• To address these challenges, we propose \textbf{SVGDreamer}, a novel framework for text-guided vector graphics synthesis.
• Our method introduces a \textbf{semantic-driven image vectorization (SIVE)} process, which decomposes the generation procedure into foreground objects and background elements, thereby improving structural controllability and editability.