Generative adversarial networks (GANs) [7] are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector to a whole spatial tensor, we create an architecture with properties well suited to the task of texture synthesis, which we call spatial GAN (SGAN). To our knowledge, this is the first successful completely data-driven texture synthesis method based on GANs. Our method has the following features which make it a state of the art algorithm for texture synthesis: high image quality of the generated textures, very high scalability w.r.t. the output texture size, fast real-time forward generation, the ability to fuse multiple diverse source images in complex textures. To illustrate these capabilities we present multiple experiments with different classes of texture images and use cases. We also discuss some limitations of our method with respect to the types of texture images it can synthesize, and compare it to other neural techniques for texture generation. A texture can be defined as an image containing repeating patterns with some amount of randomness. More formally, a texture is a realization of a stationary ergodic stochastic process [6] . The goal of visual texture analysis is to infer a generating process from an example texture, which then allows to generate arbitrarily many new samples of that texture -hence performing texture synthesis. Success in that task is judged primarily by visual quality and closeness to the original texture as estimated by human observers, but also by other criteria which may be application specific [17] , e.g. the speed of analysis and synthesis, ability to generate diverse textures of arbitrary size, the ability to create smoothly morphing textures. Approaches to do that fall in two broad categories. Non-parametric techniques resample pixels [3] or whole patches [2] from example textures, effectively randomizing the input texture in ways that preserve its visual perceptual properties. They can produce high quality images, but have two drawbacks: (i) they do not "learn" any models of the textures of interest but just reorder the input texture using local similarity, and (ii) they can be time-consuming when large textures should be synthesized because of all the search routines involved. There are methods to accelerate examplebased techniques [17] , but this requires complicated algorithms.
REF proposes to use a spatial GAN by extending the input noise distribution space to suit to the texture generation task.
Texture Synthesis with Spatial Generative Adversarial Networks
NIPS 2016 adversarial learning workshop, Barcelona, Spain
Computer Science Mathematics