  As image generation techniques mature, there is a growing interest in
explainable representations that are easy to understand and intuitive to
manipulate. In this work, we turn to co-occurrence statistics, which have long
been used for texture analysis, to learn a controllable texture synthesis
model. We propose a fully convolutional generative adversarial network,
conditioned locally on co-occurrence statistics, to generate arbitrarily large
images while having local, interpretable control over the texture appearance.
To encourage fidelity to the input condition, we introduce a novel
differentiable co-occurrence loss that is integrated seamlessly into our
framework in an end-to-end fashion. We demonstrate that our solution offers a
stable, intuitive and interpretable latent representation for texture
synthesis, which can be used to generate a smooth texture morph between
different textures. We further show an interactive texture tool that allows a
user to adjust local characteristics of the synthesized texture image using the
co-occurrence values directly.
