ChartReformer: Natural Language-Driven Chart Image Editing
Pengyu Yan Mahesh Bhosale Jay Lal Bikhyat Adhikari David Doermann
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.