“Non-stationary texture synthesis by adversarial expansion” by Zhou, Zhu, Bai, Lischinski, Cohen-Or, et al. …
Conference:
Type(s):
Entry Number: 49
Title:
- Non-stationary texture synthesis by adversarial expansion
Session/Category Title: Computational Photography
Presenter(s)/Author(s):
Moderator(s):
Abstract:
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.
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