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Using Generative Art to Convey Past and Future Climate Transitions
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Let the Chart Spark: Embedding Semantic Context into Chart with Text-to-Image Generative Model.

Shishi Xiao, Suizi Huang, Yue Lin

    IEEE Transactions on Visualization and Computer Graphics
    |October 25, 2023
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    Summary
    This summary is machine-generated.

    ChartSpark generates pictorial visualizations using text-to-image models, enhancing data representation. This novel system integrates semantic context and data, offering a flexible approach to visualization design.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Pictorial visualizations integrate data with semantic context for engaging information display.
    • Existing authoring tools often rely on retrieving visual elements, potentially compromising data integrity.
    • Current text-guided methods have limitations due to predefined entities.

    Purpose of the Study:

    • To propose ChartSpark, a novel system for embedding semantic context into charts using text-to-image generative models.
    • To generate pictorial visualizations conditioned on both textual semantic context and chart data.
    • To develop an interactive interface for user-friendly generation, modification, and assessment of visualizations.

    Main Methods:

    • Utilizing text-to-image generative models to create pictorial visualizations.
    • Conditioning generation on semantic context from text inputs and data from plain charts.
    • Integrating a text analyzer, editing module, and evaluation module into an interactive visual interface.

    Main Results:

    • ChartSpark generates pictorial visualizations by embedding semantic context into charts.
    • The system supports both foreground and background pictorial generation, aligning with design practices.
    • Experimental evaluation demonstrated the usability of the ChartSpark tool.

    Conclusions:

    • Text-to-image generative models combined with interactive interfaces offer significant potential for visualization design.
    • ChartSpark provides a flexible and data-integrity-preserving method for creating pictorial visualizations.
    • The developed interactive system enhances the creation and assessment of data visualizations.