Traditional visualizations often use abstract graphics, limiting understanding and memorability. Existing methods for pictorial visualization are more engaging, but often create disjointed compositions. To address this, we propose PictorialAttributes, a technique utilizing LLMs and diffusion models to depict data attributes. Examples show its promise for compelling and informative pictorial visualizations.
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Dahary et al. (2024) studied this question.
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