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.

翻译：深度学习的最新进展催生了许多成功的无监督图像到图像翻译模型，它们能够在无需配对数据的情况下学习两个视觉域之间的对应关系。然而，在不同域之间（尤其是视觉差异显著的域）构建鲁棒的映射仍然是一个巨大挑战。本文提出了一种新颖的通用框架——生成先验引导的无监督图像到图像翻译（GP-UNIT），该框架提升了现有翻译模型的质量、适用性和可控性。GP-UNIT的核心思想是从预训练的类别条件生成对抗网络中提炼生成先验，以构建粗层面的跨域对应关系，并将所学先验应用于对抗性翻译，从而挖掘细层面的对应关系。借助学习到的多层级内容对应关系，GP-UNIT能够在相近域和相远域之间执行有效的翻译。对于相近域，GP-UNIT可通过一个参数来调节翻译过程中内容对应关系的强度，使用户能够在内容一致性与风格一致性之间取得平衡。对于相远域，本文探索了半监督学习方案，以引导GP-UNIT发现仅凭外观难以学到的精确语义对应关系。通过大量实验，我们验证了GP-UNIT在多个域之间的鲁棒、高质量且多样化的翻译中优于现有先进翻译模型。