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Over the past few years, image-to-image (I2I) translation methods have beenproposed to translate a given image into diverse outputs. Despite theimpressive results, they mainly focus on the I2I translation between twodomains, so the multi-domain I2I translation still remains a challenge. Toaddress this problem, we propose a novel multi-domain unsupervisedimage-to-image translation (MDUIT) framework that leverages the decomposedcontent feature and appearance adaptive convolution to translate an image intoa target appearance while preserving the given geometric content. We alsoexploit a contrast learning objective, which improves the disentanglementability and effectively utilizes multi-domain image data in the trainingprocess by pairing the semantically similar images. This allows our method tolearn the diverse mappings between multiple visual domains with only a singleframework. We show that the proposed method produces visually diverse andplausible results in multiple domains compared to the state-of-the-art methods.

Multi-domain Unsupervised Image-to-Image Translation with Appearance Adaptive Convolution