Herein is presented a light-weight, high-quality texture synthesis algorithm that generalizes to other applications. We utilize an optimal transport optimization process within a bottleneck layer of an auto-encoder, achieving quality and flexibility on par with expensive back-propagation based neural texture synthesis methods, but at interactive rates. In addition to superior synthesis quality, our statistically motivated approach generalizes better to other special case texture synthesis problems such as Style Transfer, Inverse-Texture Synthesis, Texture Mixing, Multi-Scale Texture Synthesis, Structured Image Hybrids and Texture Painting. We treat the texture synthesis problem as the optimal transport between Probably Density Function of the deep neural activation vectors of the image being synthesized and the exemplar texture. We present a fast algorithm that matches random sliced 1-Dimensional histograms projected from the full N-Dimensional distribution and we propose an extension of this algorithm that reduces dimensionality of neural feature space.
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