Non-Stationary Texture Synthesis by Adversarial Expansion

TL;DR

This paper introduces a GAN-based method for non-stationary texture synthesis that effectively captures large-scale structures and inhomogeneous attributes, outperforming existing techniques.

Contribution

It presents a novel approach using adversarial expansion to synthesize non-stationary textures, addressing a challenge not solved by previous methods.

Findings

Successfully captures large-scale structures in non-stationary textures

Handles spatially variant and inhomogeneous textures effectively

Outperforms existing methods on challenging non-stationary textures

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.