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.

本研究提出了一种新的框架，Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT)，利用预训练的类条件生成对抗网络 (GANs) 的生成先验来跨越不同领域学习丰富的内容对应关系，并且我们还提出了一种新的粗粒度到细粒度的策略，通过在抽象的语义层面上捕捉一个强大的粗级内容表示来连接对象，进而为更准确的多层级内容对应学习适应性的细粒度内容特征。实验表明我们提出的方法优越性强，可适用于不同的领域和更挑战的任务。