  We present SeamlessGAN, a method capable of automatically generating tileable
texture maps from a single input exemplar. In contrast to most existing
methods, focused solely on solving the synthesis problem, our work tackles both
problems, synthesis and tileability, simultaneously. Our key idea is to realize
that tiling a latent space within a generative network trained using
adversarial expansion techniques produces outputs with continuity at the seam
intersection that can be then be turned into tileable images by cropping the
central area. Since not every value of the latent space is valid to produce
high-quality outputs, we leverage the discriminator as a perceptual error
metric capable of identifying artifact-free textures during a sampling process.
Further, in contrast to previous work on deep texture synthesis, our model is
designed and optimized to work with multi-layered texture representations,
enabling textures composed of multiple maps such as albedo, normals, etc. We
extensively test our design choices for the network architecture, loss function
and sampling parameters. We show qualitatively and quantitatively that our
approach outperforms previous methods and works for textures of different
types.
