We gratefully acknowledge support from the Simons Foundation, member institutions , and all contributors. Donate [Submitted on 28 Apr 2023 ( v1 ), last revised 2 Jul 2023 (this version, v2)]

Title: Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model

View PDF Abstract: 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.

Submission history

From: Xiao Shishi [ view email ]
[v1] Fri, 28 Apr 2023 05:18:30 UTC (30,175 KB)
Sun, 2 Jul 2023 11:12:56 UTC (34,244 KB)
View a PDF of the paper titled Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model, by Shishi Xiao and 4 other authors

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