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 introduces 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 address issues of shape over-smoothing, color over-saturation, limited diversity, and slow convergence of the existing text-to-SVG generation methods by modeling SVGs as distributions of control points and colors. Furthermore, VPSD leverages a reward model to re-weight vector particles, which improves aesthetic appeal and accelerates convergence. Extensive experiments are conducted to validate the effectiveness of SVGDreamer, demonstrating its superiority over baseline methods in terms of editability, visual quality, and diversity. Project page: \href{https://ximinng.github.io/SVGDreamer-project/}{https://ximinng.github.io/SVGDreamer-project/}

翻译：近期，文本引导的可缩放矢量图形（SVG）合成在图标绘制与草图等领域展现出应用前景。然而，现有文本到SVG生成方法缺乏可编辑性，且在视觉质量与结果多样性方面存在不足。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。该方法引入语义驱动图像矢量化（SIVE）流程，可将合成过程分解为前景物体与背景两部分，从而增强可编辑性。具体而言，SIVE流程引入基于注意力的图元控制与注意力掩码损失函数，以实现对单个元素的有效控制与操作。此外，我们提出基于粒子的矢量分数蒸馏（VPSD）方法，通过将SVG建模为控制点与颜色的分布，解决了现有文本到SVG生成方法中形状过度平滑、色彩过度饱和、多样性不足及收敛缓慢等问题。进一步地，VPSD利用奖励模型对矢量粒子进行重加权，从而提升美学吸引力并加速收敛。通过大量实验验证了SVGDreamer的有效性，结果表明其在可编辑性、视觉质量与多样性方面均优于基线方法。项目页面：\href{https://ximinng.github.io/SVGDreamer-project/}{https://ximinng.github.io/SVGDreamer-project/}