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Modern image generative models show remarkable sample quality when trained ona single domain or class of objects. In this work, we introduce a generativeadversarial network that can simultaneously generate aligned image samples frommultiple related domains. We leverage the fact that a variety of object classesshare common attributes, with certain geometric differences. We proposePolymorphic-GAN which learns shared features across all domains and aper-domain morph layer to morph shared features according to each domain. Incontrast to previous works, our framework allows simultaneous modelling ofimages with highly varying geometries, such as images of human faces, paintedand artistic faces, as well as multiple different animal faces. We demonstratethat our model produces aligned samples for all domains and show how it can beused for applications such as segmentation transfer and cross-domain imageediting, as well as training in low-data regimes. Additionally, we apply ourPolymorphic-GAN on image-to-image translation tasks and show that we cangreatly surpass previous approaches in cases where the geometric differencesbetween domains are large.

Polymorphic-GAN: Generating Aligned Samples across Multiple Domains with Learned Morph Maps