. Examples of two extremely challenging non-stationary textures (middle column), synthesized by our method (left and right). Note that our method succeeds in reproducing and extending the global structure and trends present in the input exemplars. 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.
In REF , Zhou et al. proposed a state-of-the-art generative approach for doubling the spatial extent of the given input patch by injecting newer texture content.
Non-stationary texture synthesis by adversarial expansion
TOGS
Computer Science