Non-stationary texture synthesis by adversarial expansion

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Summary

This paper proposes a new approach for example-based non-stationary texture synthesis that uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar, and demonstrates that it can cope with challenging textures, which no other existing method can handle.

Type
article
Published
2018-05-11
Cited by
229
References
31
Access
Open access

Keywords

Texture synthesis, Computer science, Texture (cosmology), Generator (circuit theory), Artificial intelligence

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