提升文本生成矢量图的可编辑性与多样性，支持六种风格输出。
SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generation
- 基于语义层级的矢量化框架HIVE，实现组件级精细控制。
- 引入粒子得分蒸馏VPSD，提升图像多样性和美学质量。
- 支持动态调整矢量元素数量，适合海报与图标设计。
近期，文本引导的可缩放矢量图形（SVG）生成在图标设计与草图生成领域展现出巨大潜力。然而，现有文本到SVG方法生成的图形普遍存在可编辑性差、视觉质量低及多样性不足的问题。本文提出一种新型文本引导矢量图形生成方法以解决上述问题。为提升输出图形的可编辑性，我们引入分层图像矢量化（HIVE）框架，基于语义对象层级进行优化，并通过图像分割先验指导组件级别的矢量化，实现图形元素的解耦与细粒度编辑。为提升多样性，提出基于粒子的得分蒸馏（VPSD）方法，缓解过饱和问题，结合预训练奖励模型重加权矢量粒子，增强美学表现并加速收敛。此外，设计自适应矢量原语控制策略，动态调节原语数量，更精准呈现图形细节。大量实验验证了该方法在可编辑性、视觉质量和多样性方面的优越性。结果表明，新方法支持最多六种不同矢量风格，能生成适用于风格化设计与海报创作的高质量矢量资产。代码与演示将公开于：http://ximinng.github.io/SVGDreamerV2Project/
原文摘要 · Abstract (English)
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/
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