  Unsupervised image-to-image translation is an important and challenging
problem in computer vision. Given an image in the source domain, the goal is to
learn the conditional distribution of corresponding images in the target
domain, without seeing any pairs of corresponding images. While this
conditional distribution is inherently multimodal, existing approaches make an
overly simplified assumption, modeling it as a deterministic one-to-one
mapping. As a result, they fail to generate diverse outputs from a given source
domain image. To address this limitation, we propose a Multimodal Unsupervised
Image-to-image Translation (MUNIT) framework. We assume that the image
representation can be decomposed into a content code that is domain-invariant,
and a style code that captures domain-specific properties. To translate an
image to another domain, we recombine its content code with a random style code
sampled from the style space of the target domain. We analyze the proposed
framework and establish several theoretical results. Extensive experiments with
comparisons to the state-of-the-art approaches further demonstrates the
advantage of the proposed framework. Moreover, our framework allows users to
control the style of translation outputs by providing an example style image.
Code and pretrained models are available at https://github.com/nvlabs/MUNIT
