Transgaga: Geometry-aware unsupervised image-to-image translation

Unsupervised image-to-image translation aims at learning a mapping between two visual domains. However, learning a translation across large geometry variations al-ways 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 in-troduce a geometry prior loss and a conditional VAE loss to encourage the network to learn independent but com-plementary 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 ap-pearance references, our method also supports multimodal translation. Project page: https://wywu.github. io/projects/TGaGa/TGaGa.html.