  In the past decade, exemplar-based texture synthesis algorithms have seen
strong gains in performance by matching statistics of deep convolutional neural
networks. However, these algorithms require regularization terms or user-added
spatial tags to capture long range constraints in images. Having access to a
user-added spatial tag for all situations is not always feasible, and
regularization terms can be difficult to tune. Thus, we propose a new set of
statistics for texture synthesis based on Sliced Wasserstein Loss, create a
multi-scale method to synthesize textures without a user-added spatial tag,
study the ability of our proposed method to capture long range constraints, and
compare our results to other optimization-based, single texture synthesis
algorithms.
