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: https://ximinng.github.io/SVGDreamer-project/

翻译：近年来，文本引导的可缩放矢量图形（SVG）合成在图标设计和草图生成等领域展现出潜力。然而，现有的文本到SVG生成方法缺乏可编辑性，且在视觉质量和结果多样性方面存在不足。为克服这些局限性，本文提出了一种新颖的文本引导矢量图形合成方法SVGDreamer。该方法引入了语义驱动的图像矢量化（SIVE）过程，将合成内容分解为前景对象和背景，从而增强可编辑性。具体而言，SIVE过程通过基于注意力的图元控制机制和注意力掩码损失函数，实现对单个图形元素的有效控制与操纵。此外，我们提出基于矢量粒子的分数蒸馏（VPSD）方法，通过将SVG建模为控制点与颜色的概率分布，解决了现有文本到SVG生成方法中存在的形状过度平滑、色彩过饱和、多样性受限及收敛速度慢等问题。VPSD进一步利用奖励模型对矢量粒子进行重加权，在提升视觉美感的同时加速收敛过程。大量实验验证了SVGDreamer的有效性，其在可编辑性、视觉质量和多样性方面均优于基线方法。项目页面：https://ximinng.github.io/SVGDreamer-project/