Learning to Draw Is Learning to See: Analyzing Eye Tracking Patterns for Assisted Observational Drawing

Drawing is an artistic process involving extensive observation. Understanding how professional artists observe as they draw has significant value because it offers insight into their perception patterns and acquired skills. While previous studies used eye tracking to analyze the drawing process, they fell short in aligning gaze data with drawing actions due to the spatial and temporal gaps between observation and drawing in a model-to-paper setup. This paper presents a study in an image-to-image setup, in which artists observe a reference image and draw on a blank canvas on a tablet, capturing a clearer mapping between eye movements and drawn strokes. Our analysis demonstrates a strong spatial correlation between observed regions and corresponding strokes. We further find that artists initially follow a more structured region-by-region approach and then switch to a less constrained sequence for details. Based on these findings, we develop an assistive interface that integrates real-time visual guidance from professional artists’ eye tracking data, enabling novices to emulate their observation and drawing strategies. A user study shows that novices can draw significantly more accurate shapes using our assistive interface, highlighting the importance of modeling observation and the potential of leveraging eye tracking data in future educational and creativity support tools. Our datasets, analysis code, and assistive interface are available at https://github.com/CISLab-HKUST/Learning-to-Draw-Is-Learning-to-See.