  We introduce a segmentation-guided approach to synthesise images that
integrate features from two distinct domains. Images synthesised by our
dual-domain model belong to one domain within the semantic mask, and to another
in the rest of the image - smoothly integrated. We build on the successes of
few-shot StyleGAN and single-shot semantic segmentation to minimise the amount
of training required in utilising two domains. The method combines a few-shot
cross-domain StyleGAN with a latent optimiser to achieve images containing
features of two distinct domains. We use a segmentation-guided perceptual loss,
which compares both pixel-level and activations between domain-specific and
dual-domain synthetic images. Results demonstrate qualitatively and
quantitatively that our model is capable of synthesising dual-domain images on
a variety of objects (faces, horses, cats, cars), domains (natural, caricature,
sketches) and part-based masks (eyes, nose, mouth, hair, car bonnet). The code
is publicly available at:
https://github.com/denabazazian/Dual-Domain-Synthesis.
