ChartReformer: Natural Language-Driven Chart Image Editing

TL;DR

ChartReformer enables natural language-based editing of chart images by understanding and modifying visual and data attributes without needing original data, covering style, layout, and data changes.

Contribution

It introduces a novel method that comprehends chart images and generates new visual and data attributes based on language prompts, allowing versatile chart editing.

Findings

Effective editing of chart images using natural language prompts

Model can generate underlying data tables and visual attributes for new charts

Promising experimental results demonstrate the approach's potential

Abstract

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.