ZotWeb	article-journal
Src Url	Zhou, Zhu, Bai, Lischinski, Cohen-Or, Huang (2018)

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.

---

TextureSynthesis

Citer: (Zhou et al., 2018)

FTag: Zhou-et-al.-2018

APA7: Zhou, Y., Zhu, Z., Bai, X., Lischinski, D., Cohen-Or, D., & Huang, H. (2018). Non-Stationary Texture Synthesis by Adversarial Expansion. _ArXiv:1805.04487 [Cs] _. http://arxiv.org/abs/1805.04487

https://app.simplenote.com/p/GZW500

Computing methodologies --> Appearance and texture representations ; Image manipulation ; Texturing ;
TexSynt

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.

The missing resolution is invented by the network

convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks
TexSynt

We have presented an example-based texture synthesis method capable of expanding an exemplar texture, while faithfully preserving the global structures therein. This is achieved by training a generative adversarial network, whose generator learns how to expand small sub windows of the exemplar to the larger texture windows containing them. A variety of results demonstrate that, through such adversarial training, the generator is able to faithfully repro-duce local patterns, as well as their global arrangements. Although a dedicated generator must be trained for each exemplar, once it is trained, synthesis is extremely fast, requiring only a single feed-forward pass through the generator network. The trained model is stable enough for repeated application, enabling generating diverse results of different sizes.

---

---