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 visualization abstraction transformer that converts static visualizations into flexible and interactive representations regardless of their underlying implementation. Athanor allows users to effortlessly author interactions through natural language instructions, eliminating the need for programming. We conducted two case studies and in-depth interviews with target users to evaluate our approach. The results demonstrate the effectiveness and usability of our approach in allowing users to conveniently enable flexible interactions for static visualizations.

翻译：交互性对于有效的数据可视化至关重要。然而，为现有的静态可视化实现交互通常具有挑战性，因为现有静态可视化的底层代码和数据通常不可用，即使原始代码和数据可用，为其启用交互也需要大量的时间和精力。为填补这一空白，我们提出了 Athanor，一种利用多模态大语言模型和自然语言指令将现有静态可视化转换为交互式可视化的新方法。我们的方法引入了三个关键创新：(1) 一个动作-修改交互设计空间，将可视化交互映射为用户动作及相应的调整；(2) 一个多智能体需求分析器，将自然语言指令翻译为可操作的操作空间；(3) 一个可视化抽象转换器，无论底层实现如何，都能将静态可视化转换为灵活且可交互的表示形式。Athanor 允许用户通过自然语言指令轻松创作交互，无需编程。我们进行了两个案例研究，并对目标用户进行了深度访谈以评估我们的方法。结果表明，我们的方法在允许用户方便地为静态可视化启用灵活交互方面具有有效性和可用性。