Reviving Static Charts into Live Charts
Lu Ying, Yun Wang, Haotian Li, Shuguang Dou, Haidong Zhang, Xinyang, Jiang, Huamin Qu, and Yingcai Wu

TL;DR
This paper presents 'Live Charts,' an innovative method that transforms static data charts into interactive, animated, and narrated visualizations to enhance understanding and engagement.
Contribution
The paper introduces an automated approach combining GNNs and large language models to convert static charts into multi-sensory Live Charts.
Findings
Live Charts improve data comprehension and engagement.
User studies show preference for Live Charts over static charts.
The approach effectively analyzes and animates chart components.
Abstract
Data charts are prevalent across various fields due to their efficacy in conveying complex data relationships. However, static charts may sometimes struggle to engage readers and efficiently present intricate information, potentially resulting in limited understanding. We introduce "Live Charts," a new format of presentation that decomposes complex information within a chart and explains the information pieces sequentially through rich animations and accompanying audio narration. We propose an automated approach to revive static charts into Live Charts. Our method integrates GNN-based techniques to analyze the chart components and extract data from charts. Then we adopt large natural language models to generate appropriate animated visuals along with a voice-over to produce Live Charts from static ones. We conducted a thorough evaluation of our approach, which involved the model…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Analysis and Summarization · Music and Audio Processing · Data Visualization and Analytics
