  Colorization has attracted increasing interest in recent years. Classic
reference-based methods usually rely on external color images for plausible
results. A large image database or online search engine is inevitably required
for retrieving such exemplars. Recent deep-learning-based methods could
automatically colorize images at a low cost. However, unsatisfactory artifacts
and incoherent colors are always accompanied. In this work, we propose
GCP-Colorization that leverages the rich and diverse color priors encapsulated
in a pretrained Generative Adversarial Networks (GAN) for automatic
colorization. Specifically, we first "retrieve" matched features (similar to
exemplars) via a GAN encoder and then incorporate these features into the
colorization process with feature modulations. Thanks to the powerful
generative color prior (GCP) and delicate designs, our GCP-Colorization could
produce vivid colors with a single forward pass. Moreover, it is highly
convenient to obtain diverse results by modifying GAN latent codes.
GCP-Colorization also inherits the merit of interpretable controls of GANs and
could attain controllable and smooth transitions by walking through GAN latent
space. Extensive experiments and user studies demonstrate that GCP-Colorization
achieves superior performance than previous works. Codes are available at
https://github.com/ToTheBeginning/GCP-Colorization.
