Non-Stationary Texture Synthesis by Adversarial Expansion (1805.04487v1)

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.

• The paper introduces a fully convolutional generator that expands texture blocks to create realistic non-stationary textures.
• It employs a GAN architecture with adversarial, L1, and style losses to effectively double spatial extents of texture samples.
• The method preserves large-scale structures, offering enhanced realism and faster rendering for computer graphics and related applications.

Insights on Non-Stationary Texture Synthesis by Adversarial Expansion

The paper "Non-Stationary Texture Synthesis by Adversarial Expansion" by Yang Zhou et al. introduces a notable advancement in the domain of example-based texture synthesis, focusing on non-stationary textures. The authors address the inherent challenges posed by non-stationary textures, which exhibit large-scale structures and spatial variance, by proposing a novel method leveraging generative adversarial networks (GANs).

The primary contribution of this paper is the development of a fully convolutional generator network, which is trained to expand texture blocks by doubling their spatial extent. This network is capable not only of enlarging the entire exemplar texture but can extend any of its sub-blocks as well. The generator network is trained in a self-supervised manner where it receives smaller texture blocks as input and learns to produce larger blocks that are visually similar to the corresponding sections from the input exemplar. This process is guided by a discriminator network that distinguishes between real and generated texture blocks. The efficacy of the method is elucidated through empirical results demonstrating the synthesis of challenging non-stationary textures, with the preservation and extension of large-scale structures that existing methods struggle to replicate.

Technically, the method involves training a GAN where the generator network relies on a sequence of convolutional and residual layers, conducive to capturing the extensive receptive field required to model the non-stationary behavior across texture exemplars. The discriminator network utilizes a PatchGAN architecture that effectively assesses local patch realness. The training objective combines adversarial loss, $L_1$ loss, and style loss from a pre-trained VGG-19 model that ensures the synthesized textures maintain the statistical properties of the exemplars.

The implications of this research are substantial in areas where realism in texture generation is crucial, such as computer graphics, virtual reality, and augmented reality applications. The authors propose that applying such adversarial expansion techniques enables faster rendering processes as large textures can be generated from smaller examples with minimal computational time post-training. Moreover, the fully convolutional nature of the network permits scalability in texture synthesis well beyond typical exemplar sizes, demonstrating its potential for real-time applications once the model is trained.

Future investigations could target improving the training efficiency and robustness of the network, particularly for exemplars with limited pattern representation. Additionally, exploring methods that can further diversify output textures without additional retraining could expand its usability in diverse environments.

In conclusion, the paper presents a systematic approach to generating non-stationary textures by employing generative adversarial networks, showcasing substantial improvements over existing methods in terms of both speed and quality of generated textures. Through adversarial expansion, the authors demonstrate the potential for GANs to revolutionize texture synthesis by not only addressing the scalability issue but also by enriching the visual complexity that can be achieved at large scales.