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.

本文提出了一种新颖的多功能框架 —— 生成先验引导的非监督图像到图像翻译（GP-UNIT），通过学习多层级内容对应，可以在各种领域中实现高质量、多样化的翻译。

GP-UNIT: 通用的生成式先验用于多功能无监督图像到图像翻译

GP-UNIT: Generative Prior for Versatile Unsupervised Image-to-Image  Translation

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。