Spline-FRIDA: Towards Diverse, Humanlike Robot Painting Styles with a Sample-Efficient, Differentiable Brush Stroke Model

TL;DR

Spline-FRIDA is a novel, sample-efficient model that captures complex human brush stroke styles for robotic painting, enhancing artistic diversity and realism in robot-generated art.

Contribution

It introduces a new brush stroke dynamics model using motion capture and autoencoders, enabling robots to produce more humanlike and diverse painting styles.

Findings

Spline-FRIDA's strokes are more human-like and artistic.

The approach improves semantic planning in robotic painting.

Survey confirms better style capture and realism.

Abstract

A painting is more than just a picture on a wall; a painting is a process comprised of many intentional brush strokes, the shapes of which are an important component of a painting's overall style and message. Prior work in modeling brush stroke trajectories either does not work with real-world robotics or is not flexible enough to capture the complexity of human-made brush strokes. In this work, we introduce Spline-FRIDA which can model complex human brush stroke trajectories. This is achieved by recording artists drawing using motion capture, modeling the extracted trajectories with an autoencoder, and introducing a novel brush stroke dynamics model to the existing robotic painting platform FRIDA. We conducted a survey and found that our open-source Spline-FRIDA approach successfully captures the stroke styles in human drawings and that Spline-FRIDA's brush strokes are more human-like, improve semantic planning, and are more artistic compared to existing robot painting systems with restrictive B\'ezier curve strokes.