  Unsupervised image-to-image translation aims to learn the translation between
two visual domains without paired data. Despite the recent progress in image
translation models, it remains challenging to build mappings between complex
domains with drastic visual discrepancies. In this work, we present a novel
framework, Generative Prior-guided UNsupervised Image-to-image Translation
(GP-UNIT), to improve the overall quality and applicability of the translation
algorithm. Our key insight is to leverage the generative prior from pre-trained
class-conditional GANs (e.g., BigGAN) to learn rich content correspondences
across various domains. We propose a novel coarse-to-fine scheme: we first
distill the generative prior to capture a robust coarse-level content
representation that can link objects at an abstract semantic level, based on
which fine-level content features are adaptively learned for more accurate
multi-level content correspondences. Extensive experiments demonstrate the
superiority of our versatile framework over state-of-the-art methods in robust,
high-quality and diversified translations, even for challenging and distant
domains.
