From Static to Interactive: Authoring Interactive Visualizations via Natural Language
Can Liu, Jaeuk Lee, Tianhe Chen, Zhibang Jiang, Xiaolin Wen, Yong Wang

TL;DR
Athanor leverages multimodal large language models to convert static visualizations into interactive ones through natural language, simplifying the authoring process without programming.
Contribution
It introduces a novel approach combining a requirement analyzer and visualization transformer to enable interaction authoring via natural language for static visualizations.
Findings
Effective transformation of static to interactive visualizations demonstrated.
User interviews show high usability and convenience.
Case studies confirm the approach's practicality.
Abstract
Interactivity is crucial for effective data visualizations. However, it is often challenging to implement interactions for existing static visualizations, since the underlying code and data for existing static visualizations are often not available, and it also takes significant time and effort to enable interactions for them even if the original code and data are available. To fill this gap, we propose Athanor, a novel approach to transform existing static visualizations into interactive ones using multimodal large language models (MLLMs) and natural language instructions. Our approach introduces three key innovations: (1) an action-modification interaction design space that maps visualization interactions into user actions and corresponding adjustments, (2) a multi-agent requirement analyzer that translates natural language instructions into an actionable operational space, and (3) a…
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