Large-scale painting of photographs by interactive optimization

Abstract

We propose a system for painting large-scale murals of arbitrary input photographs. To that end, we choose spray paint, which is easy to use and affordable, yet requires skill to create interesting murals. An untrained user simply waves a programmatically actuated spray can in front of the canvas. Our system tracks the can׳s position and determines the optimal amount of paint to disperse to best approximate the input image. We accurately calibrate our spray paint simulation model in a pre-process and devise optimization routines for run-time paint dispersal decisions. Our setup is light-weight: it includes two webcams and QR-coded cubes for tracking, and a small actuation device for the spray can, attached via a 3D-printed mount. The system performs at haptic rates, which allows the user – informed by a visualization of the image residual – to guide the system interactively to recover low frequency features. We validate our pipeline for a variety of grayscale and color input images and present results in simulation and physically realized murals.