CR-UNIT: Unsupervised Image-to-Image Translation with Content Reconstruction

The goal of unsupervised image-to-image translation (UNIT) is to translate images between two different domains without any paired data. Recent research has shown great progress in the UNIT subject, but it still faces challenges when translating between artificial and natural objects. To overcome this challenge, we propose a novel unsupervised image-to-image translation with content reconstruction (CR-UNIT), a two-stage and from-coarse-to-fine training framework. Specifically, CR-UNIT builds a corresponding relationship among the content features of different domains on a coarse granularity in the first stage. In the second stage, a new content reconstruction module is constructed to extract the fine-grained content and style features, obtaining more detailed semantic correspondence and better fusion of the content and style features. Furthermore, we design a content reconstruction loss to facilitate the training of our model. Extensive experimental results demonstrate the superiority of the proposed CR-UNIT over the existing methods. Especially for the translation task between the artificial and natural objects, our CR-UNIT achieves outstanding effects in terms of perceptive quality and objective metric.