Scalable Vector Graphics (SVG) is a popular vector image format that offers good support for interactivity and animation. Despite its appealing characteristics, creating custom SVG content can be challenging for users due to the steep learning curve required to understand SVG grammars or get familiar with professional editing software. Recent advancements in text-to-image generation have inspired researchers to explore vector graphics synthesis using either image-based methods (i.e., text -> raster image -> vector graphics) combining text-to-image generation models with image vectorization, or language-based methods (i.e., text -> vector graphics script) through pretrained large language models. However, these methods still suffer from limitations in terms of generation quality, diversity, and flexibility. In this paper, we introduce IconShop, a text-guided vector icon synthesis method using autoregressive transformers. The key to success of our approach is to sequentialize and tokenize SVG paths (and textual descriptions as guidance) into a uniquely decodable token sequence. With that, we are able to fully exploit the sequence learning power of autoregressive transformers, while enabling both unconditional and text-conditioned icon synthesis. Through standard training to predict the next token on a large-scale vector icon dataset accompanied by textural descriptions, the proposed IconShop consistently exhibits better icon synthesis capability than existing image-based and language-based methods both quantitatively and qualitatively. Meanwhile, we observe a dramatic improvement in generation diversity, which is validated by the objective Uniqueness and Novelty measures. More importantly, we demonstrate the flexibility of IconShop with multiple novel icon synthesis tasks, including icon editing, icon interpolation, icon semantic combination, and icon design auto-suggestion.

翻译：可缩放矢量图形（SVG）是一种流行的矢量图像格式，支持良好的交互性和动画效果。然而，尽管其具有吸引力，但由于学习SVG语法或熟悉专业编辑软件需要较高的学习曲线，用户创建自定义SVG内容仍面临挑战。近年来文本到图像生成的进展，促使研究者探索通过基于图像的方法（即文本→光栅图像→矢量图形）结合文本到图像生成模型与图像矢量化，或基于语言的方法（即文本→矢量图形脚本）利用预训练大语言模型，来实现矢量图形合成。然而，这些方法在生成质量、多样性和灵活性方面仍存在局限性。本文提出IconShop——一种基于文本引导与自回归Transformer的矢量图标合成方法。该方法成功的关键在于将SVG路径（以及作为引导的文本描述）序列化并符号化，转化为可唯一解码的令牌序列。由此，我们能够充分挖掘自回归Transformer的序列学习能力，同时支持无条件与文本条件图标合成。通过在大规模矢量图标数据集（附有文本描述）上进行标准训练以预测下一个令牌，所提出的IconShop在定量和定性上均展现出优于现有基于图像和基于语言方法的图标合成能力。此外，我们观察到生成多样性显著提升，这通过客观的“独特性”和“新颖性”指标得到验证。更关键的是，我们展示了IconShop在多种新型图标合成任务中的灵活性，包括图标编辑、图标插值、图标语义组合以及图标设计自动建议。