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.

翻译：数据图表因其在传达复杂数据关系方面的有效性而广泛应用于各个领域。然而，静态图表有时难以吸引读者并高效呈现复杂信息，可能导致理解受限。我们提出"动态图表"（Live Charts）这一新型呈现格式，通过丰富的动画和伴随的语音解说，逐步分解图表中的复杂信息并加以阐释。我们提出一种自动化方法，将静态图表转化为动态图表。该方法融合基于图神经网络（GNN）的技术来分析图表组件并提取图表数据，随后采用大型自然语言模型生成合适的动画视觉元素及旁白，从而将静态图表转化为动态图表。我们对该方法进行了全面评估，包括模型性能测试、用例分析、众包用户研究及专家访谈。结果表明，动态图表能够提供多感官体验，使读者更易跟进信息并理解数据洞察。我们分析了动态图表相较于静态图表作为一种新型信息消费体验的优势与不足。