Recently, text-guided scalable vector graphics (SVGs) synthesis has shown promise in domains such as iconography and sketch. However, existing text-to-SVG generation methods lack editability and struggle with visual quality and result diversity. To address these limitations, we propose a novel text-guided vector graphics synthesis method called SVGDreamer. SVGDreamer incorporates a semantic-driven image vectorization (SIVE) process that enables the decomposition of synthesis into foreground objects and background, thereby enhancing editability. Specifically, the SIVE process introduce attention-based primitive control and an attention-mask loss function for effective control and manipulation of individual elements. Additionally, we propose a Vectorized Particle-based Score Distillation (VPSD) approach to tackle the challenges of shape over-smoothing, color over-saturation, limited diversity in results, and slow convergence in existing text-to-SVG generation methods. VPSD models SVGs as distributions of control points and colors to counteract over-smoothing and over-saturation. Furthermore, VPSD leverages a reward model to reweight vector particles, which improves aesthetic appeal and accelerates convergence. Extensive experiments have been conducted to validate the effectiveness of SVGDreamer, demonstrating its superiority over baseline methods in terms of editability, visual quality, and diversity. The code and demo of SVGDreamer can be found at https://ximinng.github.io/SVGDreamer-project/

翻译：最近，文本引导的可缩放矢量图形（SVG）合成在图示法和草图等领域展现出前景。然而，现有文本到SVG的生成方法缺乏可编辑性，并在视觉质量和结果多样性方面存在困难。为解决这些局限，我们提出一种名为SVGDreamer的新型文本引导矢量图形合成方法。SVGDreamer融合了语义驱动图像矢量化（SIVE）过程，可将合成分解为前景对象和背景，从而增强可编辑性。具体而言，SIVE过程引入了基于注意力的基元控制和注意力掩码损失函数，以实现对单个元素的有效控制和操控。此外，我们提出一种基于矢量化粒子的分数蒸馏（VPSD）方法，以应对现有文本到SVG生成方法中形状过度平滑、色彩过度饱和、结果多样性有限以及收敛缓慢的挑战。VPSD将SVG建模为控制点和颜色的分布，以对抗过度平滑和过度饱和。进一步地，VPSD利用奖励模型对矢量粒子进行重加权，从而提升审美吸引力并加速收敛。我们进行了大量实验以验证SVGDreamer的有效性，结果表明其在可编辑性、视觉质量和多样性方面均优于基线方法。SVGDreamer的代码和演示可访问https://ximinng.github.io/SVGDreamer-project/。