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 color over-saturation, vector primitives over-smoothing, and limited result diversity in existing text-to-SVG generation methods. Furthermore, on the basis of VPSD, we introduce Reward Feedback Learning (ReFL) to accelerate VPSD convergence and improve aesthetic appeal. 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 \href{https://ximinng.github.io/SVGDreamer-project/}{https://ximinng.github.io/SVGDreamer-project/}.

翻译：近年来，文本引导的可缩放矢量图形（SVG）合成在图标绘制和草图等领域展现出应用潜力。然而，现有文本到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。为解决这些问题，我们提出了一种名为SVGDreamer的新型文本引导矢量图形合成方法。该方法通过语义驱动图像矢量化（SIVE）流程，将合成过程解构为前景物体与背景两个层次，从而增强可编辑性。具体而言，SIVE流程引入了基于注意力的图元控制模块与注意力掩码损失函数，实现对单个元素的有效操控。此外，我们提出基于矢量化粒子的分数蒸馏（VPSD）方法，以解决现有文本到SVG生成方法中存在的颜色过饱和、矢量图元过平滑以及结果多样性有限等挑战。在VPSD基础上，进一步引入奖励反馈学习（ReFL）以加速VPSD收敛并提升美学效果。大量实验验证了SVGDreamer的有效性，证明其在可编辑性、视觉质量和多样性方面均优于基线方法。SVGDreamer的代码和演示可在\href{https://ximinng.github.io/SVGDreamer-project/}{https://ximinng.github.io/SVGDreamer-project/}获取。