  We present a novel texture synthesis framework, enabling the generation of
infinite, high-quality 3D textures given a 2D exemplar image. Inspired by
recent advances in natural texture synthesis, we train deep neural models to
generate textures by non-linearly combining learned noise frequencies. To
achieve a highly realistic output conditioned on an exemplar patch, we propose
a novel loss function that combines ideas from both style transfer and
generative adversarial networks. In particular, we train the synthesis network
to match the Gram matrices of deep features from a discriminator network. In
addition, we propose two architectural concepts and an extrapolation strategy
that significantly improve generalization performance. In particular, we inject
both model input and condition into hidden network layers by learning to scale
and bias hidden activations. Quantitative and qualitative evaluations on a
diverse set of exemplars motivate our design decisions and show that our system
performs superior to previous state of the art. Finally, we conduct a user
study that confirms the benefits of our framework.
