HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms

Understand

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.