Core Concepts
Innovative method for text-guided vector graphics synthesis.
Abstract
SVGDreamer introduces a novel approach to text-guided scalable vector graphics synthesis, addressing limitations in existing methods. The method incorporates semantic-driven image vectorization and Vectorized Particle-based Score Distillation to enhance editability, visual quality, and diversity of generated vector graphics. Extensive experiments validate the effectiveness of SVGDreamer over baseline methods.
Stats
Scalable Vector Graphics (SVGs) offer compact file sizes suitable for visual design applications.
Existing text-to-SVG generation methods lack editability and struggle with visual quality and result diversity.
SVGDreamer introduces a semantic-driven image vectorization process for effective control and manipulation of individual elements.
Vectorized Particle-based Score Distillation models SVGs as distributions of control points and colors to counteract over-smoothing and over-saturation.
Extensive experiments demonstrate the superiority of SVGDreamer over baseline methods in terms of editability, visual quality, and diversity.
Quotes
"SVGDreamer incorporates a semantic-driven image vectorization process that enhances editability."
"VPSD models SVGs as distributions of control points and colors to counteract over-smoothing."
"Extensive experiments validate the effectiveness of SVGDreamer over baseline methods."