From Static to Interactive: Authoring Interactive Visualizations via Natural Language

📰 ArXiv cs.AI

Learn to create interactive visualizations from static ones using natural language with Athanor, making data analysis more engaging and effective

intermediate Published 28 Apr 2026

Action Steps

Apply natural language processing to identify interactive elements in static visualizations
Use Athanor to generate interactive visualization code from static visualizations
Configure interactive visualization parameters using natural language inputs
Test and refine interactive visualizations for optimal user experience
Deploy interactive visualizations to web or mobile platforms for wider accessibility

Who Needs to Know This

Data scientists and visualization experts can benefit from Athanor to enhance their static visualizations and make them more interactive, improving user experience and insights

Key Insight

💡 Athanor enables the creation of interactive visualizations from static ones using natural language, reducing the need for manual coding and increasing accessibility

Key Takeaways

Learn to create interactive visualizations from static ones using natural language with Athanor, making data analysis more engaging and effective

Full Article

Title: From Static to Interactive: Authoring Interactive Visualizations via Natural Language

Abstract:
arXiv:2601.17736v2 Announce Type: replace-cross 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 transf

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