Abstract
Colorization is an ambiguous problem, with multiple viable colorizations for a single grey-level image. However,
previous methods only produce the single most probable
colorization. Our goal is to model the diversity intrinsic
to the problem of colorization and produce multiple colorizations that display long-scale spatial co-ordination. We
learn a low dimensional embedding of color fields using a
variational autoencoder (VAE). We construct loss terms for
the VAE decoder that avoid blurry outputs and take into account the uneven distribution of pixel colors. Finally, we
build a conditional model for the multi-modal distribution
between grey-level image and the color field embeddings.
Samples from this conditional model result in diverse colorization. We demonstrate that our method obtains better diverse colorizations than a standard conditional variational autoencoder (CVAE) model, as well as a recently proposed conditional generative adversarial network (cGAN)