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arXiv · 2309.02967

Reviving Static Charts into Live Charts

Lu Ying·Yun Wang·Haotian Li·Shuguang Dou·Haidong Zhang·Xinyang Jiang·Huamin Qu·Yingcai Wu
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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 performance, use cases, a crowd-sourced user study, and expert interviews. The results demonstrate Live Charts offer a multi-sensory experience where readers can follow the information and understand the data insights better. We analyze the benefits and drawbacks of Live Charts over static charts as a new information consumption experience.

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Lu Ying, Yun Wang, Haotian Li, Shuguang Dou, Haidong Zhang, Xinyang Jiang, Huamin Qu, Yingcai Wu. 2024-05-17. Reviving Static Charts into Live Charts. https://doi.org/10.1109/tvcg.2024.3397004

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