  In this paper, we propose an approach based on domain conditional
normalization (DCN) for zero-pair image-to-image translation, i.e., translating
between two domains which have no paired training data available but each have
paired training data with a third domain. We employ a single generator which
has an encoder-decoder structure and analyze different implementations of
domain conditional normalization to obtain the desired target domain output.
The validation benchmark uses RGB-depth pairs and RGB-semantic pairs for
training and compares performance for the depth-semantic translation task. The
proposed approaches improve in qualitative and quantitative terms over the
compared methods, while using much fewer parameters. Code available at
https://github.com/samarthshukla/dcn
