  We present a new approach for universal texture synthesis by incorporating a
multi-scale texton broadcasting module in the StyleGAN-2 framework. The texton
broadcasting module introduces an inductive bias, enabling generation of
broader range of textures, from those with regular structures to completely
stochastic ones. To train and evaluate the proposed approach, we construct a
comprehensive high-resolution dataset that captures the diversity of natural
textures as well as stochastic variations within each perceptually uniform
texture. Experimental results demonstrate that the proposed approach yields
significantly better quality textures than the state of the art. The ultimate
goal of this work is a comprehensive understanding of texture space.
