  Unsupervised image-to-image translation aims at learning a mapping between
two visual domains. However, learning a translation across large geometry
variations always ends up with failure. In this work, we present a novel
disentangle-and-translate framework to tackle the complex objects
image-to-image translation task. Instead of learning the mapping on the image
space directly, we disentangle image space into a Cartesian product of the
appearance and the geometry latent spaces. Specifically, we first introduce a
geometry prior loss and a conditional VAE loss to encourage the network to
learn independent but complementary representations. The translation is then
built on appearance and geometry space separately. Extensive experiments
demonstrate the superior performance of our method to other state-of-the-art
approaches, especially in the challenging near-rigid and non-rigid objects
translation tasks. In addition, by taking different exemplars as the appearance
references, our method also supports multimodal translation. Project page:
https://wywu.github.io/projects/TGaGa/TGaGa.html
