  Recent advances in deep learning have witnessed many successful unsupervised
image-to-image translation models that learn correspondences between two visual
domains without paired data. However, it is still a great challenge to build
robust mappings between various domains especially for those with drastic
visual discrepancies. In this paper, we introduce a novel versatile framework,
Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), that
improves the quality, applicability and controllability of the existing
translation models. The key idea of GP-UNIT is to distill the generative prior
from pre-trained class-conditional GANs to build coarse-level cross-domain
correspondences, and to apply the learned prior to adversarial translations to
excavate fine-level correspondences. With the learned multi-level content
correspondences, GP-UNIT is able to perform valid translations between both
close domains and distant domains. For close domains, GP-UNIT can be
conditioned on a parameter to determine the intensity of the content
correspondences during translation, allowing users to balance between content
and style consistency. For distant domains, semi-supervised learning is
explored to guide GP-UNIT to discover accurate semantic correspondences that
are hard to learn solely from the appearance. We validate the superiority of
GP-UNIT over state-of-the-art translation models in robust, high-quality and
diversified translations between various domains through extensive experiments.
