  We propose an auto-encoder architecture for multi-texture synthesis. The
approach relies on both a compact encoder accounting for second order neural
statistics and a generator incorporating adaptive periodic content. Images are
embedded in a compact and geometrically consistent latent space, where the
texture representation and its spatial organisation are disentangled. Texture
synthesis and interpolation tasks can be performed directly from these latent
codes. Our experiments demonstrate that our model outperforms state-of-the-art
feed-forward methods in terms of visual quality and various texture related
metrics.
