Pictorial visualization seamlessly integrates data and semantic context into visual representation, conveying complex information in a manner that is both engaging and informative. Extensive studies have been devoted to developing authoring tools to simplify the creation of pictorial visualizations. However, mainstream works mostly follow a retrieving-and-editing pipeline that heavily relies on retrieved visual elements from a dedicated corpus, which often compromise the data integrity. Text-guided generation methods are emerging, but may have limited applicability due to its predefined recognized entities. In this work, we propose ChartSpark, a novel system that embeds semantic context into chart based on text-to-image generative model. ChartSpark generates pictorial visualizations conditioned on both semantic context conveyed in textual inputs and data information embedded in plain charts. The method is generic for both foreground and background pictorial generation, satisfying the design practices identified from an empirical research into existing pictorial visualizations. We further develop an interactive visual interface that integrates a text analyzer, editing module, and evaluation module to enable users to generate, modify, and assess pictorial visualizations. We experimentally demonstrate the usability of our tool, and conclude with a discussion of the potential of using text-to-image generative model combined with interactive interface for visualization design.

翻译：图示可视化将数据与语义上下文无缝融合到视觉表示中，以引人入胜且信息丰富的方式传达复杂信息。大量研究致力于开发创作工具以简化图示可视化的创建过程。然而，主流工作大多遵循检索-编辑的流程，该流程严重依赖于从专用语料库中检索的视觉元素，这往往会损害数据完整性。基于文本引导的生成方法正在兴起，但由于其预定义的已识别实体，可能适用性有限。在这项工作中，我们提出了ChartSpark，一种基于文本到图像生成模型将语义上下文嵌入图表的新颖系统。ChartSpark生成图示可视化，其条件既包括文本输入中传达的语义上下文，也包括普通图表中嵌入的数据信息。该方法适用于前景和背景图示生成，满足我们从现有图示可视化的实证研究中识别出的设计实践。我们进一步开发了一个交互式视觉界面，集成了文本分析器、编辑模块和评估模块，使用户能够生成、修改和评估图示可视化。我们通过实验证明了我们工具的可用性，并讨论了将文本到图像生成模型与交互式界面结合用于可视化设计的潜力。