  The real world exhibits an abundance of non-stationary textures. Examples
include textures with large-scale structures, as well as spatially variant and
inhomogeneous textures. While existing example-based texture synthesis methods
can cope well with stationary textures, non-stationary textures still pose a
considerable challenge, which remains unresolved. In this paper, we propose a
new approach for example-based non-stationary texture synthesis. Our approach
uses a generative adversarial network (GAN), trained to double the spatial
extent of texture blocks extracted from a specific texture exemplar. Once
trained, the fully convolutional generator is able to expand the size of the
entire exemplar, as well as of any of its sub-blocks. We demonstrate that this
conceptually simple approach is highly effective for capturing large-scale
structures, as well as other non-stationary attributes of the input exemplar.
As a result, it can cope with challenging textures, which, to our knowledge, no
other existing method can handle.
