  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.
