  Recently, there has been an increasing interest in image editing methods that
employ pre-trained unconditional image generators (e.g., StyleGAN). However,
applying these methods to translate images to multiple visual domains remains
challenging. Existing works do not often preserve the domain-invariant part of
the image (e.g., the identity in human face translations), they do not usually
handle multiple domains, or do not allow for multi-modal translations. This
work proposes an implicit style function (ISF) to straightforwardly achieve
multi-modal and multi-domain image-to-image translation from pre-trained
unconditional generators. The ISF manipulates the semantics of an input latent
code to make the image generated from it lying in the desired visual domain.
Our results in human face and animal manipulations show significantly improved
results over the baselines. Our model enables cost-effective multi-modal
unsupervised image-to-image translations at high resolution using pre-trained
unconditional GANs. The code and data are available at:
\url{https://github.com/yhlleo/stylegan-mmuit}.
