  This paper introduces a neural style transfer model to generate a stylized
image conditioning on a set of examples describing the desired style. The
proposed solution produces high-quality images even in the zero-shot setting
and allows for more freedom in changes to the content geometry. This is made
possible by introducing a novel Two-Stage Peer-Regularization Layer that
recombines style and content in latent space by means of a custom graph
convolutional layer. Contrary to the vast majority of existing solutions, our
model does not depend on any pre-trained networks for computing perceptual
losses and can be trained fully end-to-end thanks to a new set of cyclic losses
that operate directly in latent space and not on the RGB images. An extensive
ablation study confirms the usefulness of the proposed losses and of the
Two-Stage Peer-Regularization Layer, with qualitative results that are
competitive with respect to the current state of the art using a single model
for all presented styles. This opens the door to more abstract and artistic
neural image generation scenarios, along with simpler deployment of the model.
