  This paper presents a novel framework for generating texture mosaics with
convolutional neural networks. Our method is called GANosaic and performs
optimization in the latent noise space of a generative texture model, which
allows the transformation of a content image into a mosaic exhibiting the
visual properties of the underlying texture manifold. To represent that
manifold, we use a state-of-the-art generative adversarial method for texture
synthesis, which can learn expressive texture representations from data and
produce mosaic images with very high resolution. This fully convolutional model
generates smooth (without any visible borders) mosaic images which morph and
blend different textures locally. In addition, we develop a new type of
differentiable statistical regularization appropriate for optimization over the
prior noise space of the PSGAN model.
