  We propose a novel approach to automatically produce multiple colorized
versions of a grayscale image. Our method results from the observation that the
task of automated colorization is relatively easy given a low-resolution
version of the color image. We first train a conditional PixelCNN to generate a
low resolution color for a given grayscale image. Then, given the generated
low-resolution color image and the original grayscale image as inputs, we train
a second CNN to generate a high-resolution colorization of an image. We
demonstrate that our approach produces more diverse and plausible colorizations
than existing methods, as judged by human raters in a "Visual Turing Test".
