Multiclass Texture Synthesis Using Generative Adversarial Networks

Generative adversarial networks as a tool for generating content are currently one of the most popular methods for content synthesis. Despite its popularity, multiple solutions suffer from the drawback of a shortage of generality. It means that trained models can usually synthesize only one specific kind of output. The usual synthesis approach for generating N different texture species requires training N models with changing training data. However, few solutions explore the synthesis of multiple types of textures. In our work, we present an alternative approach for multiclass texture synthesis. We focus on the synthesis of realistic natural non-stationary textures. Our solution divides textures into classes based on the objects they represent and allows users to control the class of synthesized textures and their appearance. Thanks to the controllable selections from latent space, we also explore possibilities of creating transitions between classes of trained textures for potential better usage in applications where texture synthesis is required.