  While generative adversarial networks (GANs) can successfully produce
high-quality images, they can be challenging to control. Simplifying GAN-based
image generation is critical for their adoption in graphic design and artistic
work. This goal has led to significant interest in methods that can intuitively
control the appearance of images generated by GANs. In this paper, we present
HistoGAN, a color histogram-based method for controlling GAN-generated images'
colors. We focus on color histograms as they provide an intuitive way to
describe image color while remaining decoupled from domain-specific semantics.
Specifically, we introduce an effective modification of the recent StyleGAN
architecture to control the colors of GAN-generated images specified by a
target color histogram feature. We then describe how to expand HistoGAN to
recolor real images. For image recoloring, we jointly train an encoder network
along with HistoGAN. The recoloring model, ReHistoGAN, is an unsupervised
approach trained to encourage the network to keep the original image's content
while changing the colors based on the given target histogram. We show that
this histogram-based approach offers a better way to control GAN-generated and
real images' colors while producing more compelling results compared to
existing alternative strategies.
