Chart visualizations are essential for data interpretation and communication; however, most charts are only accessible in image format and lack the corresponding data tables and supplementary information, making it difficult to alter their appearance for different application scenarios. To eliminate the need for original underlying data and information to perform chart editing, we propose ChartReformer, a natural language-driven chart image editing solution that directly edits the charts from the input images with the given instruction prompts. The key in this method is that we allow the model to comprehend the chart and reason over the prompt to generate the corresponding underlying data table and visual attributes for new charts, enabling precise edits. Additionally, to generalize ChartReformer, we define and standardize various types of chart editing, covering style, layout, format, and data-centric edits. The experiments show promising results for the natural language-driven chart image editing.

翻译：图表可视化对于数据解读与信息传达至关重要；然而，大多数图表仅以图像格式呈现，缺乏对应的数据表格及补充信息，难以根据不同应用场景调整其外观。为消除图表编辑对原始底层数据和信息的依赖，我们提出ChartReformer——一种自然语言驱动的图表图像编辑方案，可直接根据输入图像及给定指令提示对图表进行编辑。该方法的关键在于，我们使模型能够理解图表内容，并对提示进行推理，从而生成新图表对应的底层数据表及视觉属性，实现精准编辑。此外，为提升ChartReformer的泛化性，我们定义并标准化了涵盖样式、布局、格式及数据导向编辑等多类图表编辑任务。实验结果表明，该自然语言驱动的图表图像编辑方法取得了令人满意的效果。