Recently, text-guided scalable vector graphics (SVG) synthesis has demonstrated significant potential in domains such as iconography and sketching. However, SVGs generated from existing Text-to-SVG methods often lack editability and exhibit deficiencies in visual quality and diversity. In this paper, we propose a novel text-guided vector graphics synthesis method to address these limitations. To enhance the editability of output SVGs, we introduce a Hierarchical Image VEctorization (HIVE) framework that operates at the semantic object level and supervises the optimization of components within the vector object. This approach facilitates the decoupling of vector graphics into distinct objects and component levels. Our proposed HIVE algorithm, informed by image segmentation priors, not only ensures a more precise representation of vector graphics but also enables fine-grained editing capabilities within vector objects. To improve the diversity of output SVGs, we present a Vectorized Particle-based Score Distillation (VPSD) approach. VPSD addresses over-saturation issues in existing methods and enhances sample diversity. A pre-trained reward model is incorporated to re-weight vector particles, improving aesthetic appeal and enabling faster convergence. Additionally, we design a novel adaptive vector primitives control strategy, which allows for the dynamic adjustment of the number of primitives, thereby enhancing the presentation of graphic details. Extensive experiments validate the effectiveness of the proposed method, demonstrating its superiority over baseline methods in terms of editability, visual quality, and diversity. We also show that our new method supports up to six distinct vector styles, capable of generating high-quality vector assets suitable for stylized vector design and poster design. Code and demo will be released at: http://ximinng.github.io/SVGDreamerV2Project/

翻译：近年来，文本引导的可缩放矢量图形（SVG）合成在图标设计与草图绘制等领域展现出巨大潜力。然而，现有文本到SVG方法生成的矢量图形往往缺乏可编辑性，且在视觉质量与多样性方面存在不足。本文提出一种新颖的文本引导矢量图形合成方法以解决这些局限。为增强输出SVG的可编辑性，我们提出分层图像矢量化（HIVE）框架，该框架在语义对象层级运行，并对矢量对象内部组件的优化过程进行监督。此方法有助于将矢量图形解耦至独立的对象与组件层级。我们提出的HIVE算法结合图像分割先验知识，不仅能确保矢量图形的表征更精确，还可实现矢量对象内部的细粒度编辑功能。为提升输出SVG的多样性，我们提出基于矢量化粒子的分数蒸馏（VPSD）方法。VPSD解决了现有方法中的过饱和问题并增强了样本多样性。通过引入预训练奖励模型对矢量粒子进行重加权，既提升了美学吸引力，也实现了更快的收敛速度。此外，我们设计了一种新颖的自适应矢量图元控制策略，可动态调整图元数量，从而增强图形细节的呈现效果。大量实验验证了所提方法的有效性，其在可编辑性、视觉质量与多样性方面均优于基线方法。我们还证明新方法支持多达六种不同的矢量风格，能够生成适用于风格化矢量设计与海报设计的高质量矢量素材。代码与演示页面发布于：http://ximinng.github.io/SVGDreamerV2Project/