  In unsupervised image-to-image translation, the goal is to learn the mapping
between an input image and an output image using a set of unpaired training
images. In this paper, we propose an extension of the unsupervised
image-to-image translation problem to multiple input setting. Given a set of
paired images from multiple modalities, a transformation is learned to
translate the input into a specified domain. For this purpose, we introduce a
Generative Adversarial Network (GAN) based framework along with a multi-modal
generator structure and a new loss term, latent consistency loss. Through
various experiments we show that leveraging multiple inputs generally improves
the visual quality of the translated images. Moreover, we show that the
proposed method outperforms current state-of-the-art unsupervised
image-to-image translation methods.
