Abstract:
Generative adversarial networks have achieved great
success in unpaired image-to-image translation. Cycle consistency allows modeling the relationship between two distinct domains without paired data. In this paper, we propose
an alternative framework, as an extension of latent space
interpolation, to consider the intermediate region between
two domains during translation. It is based on the fact that
in a flat and smooth latent space, there exist many paths
that connect two sample points. Properly selecting paths
makes it possible to change only certain image attributes,
which is useful for generating intermediate images between
the two domains. We also show that this framework can be
applied to multi-domain and multi-modal translation. Extensive experiments
Authors: Ying-Cong Chen, Xiaogang Xu, Zhuotao Tian,
Jiaya Jia
