We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset. To ensure a dataset coverage, we use an adversarial loss function that penalizes for incorrect reproductions of a given texture. In experiments, we show that our model can learn descriptive texture manifolds for large datasets and from raw data such as a collection of high-resolution photos. Moreover, we apply our method to produce 3D textures and show that it outperforms existing baselines.
@article{arxiv.1904.04751,
title = {User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks},
author = {Aibek Alanov and Max Kochurov and Denis Volkhonskiy and Daniil Yashkov and Evgeny Burnaev and Dmitry Vetrov},
journal= {arXiv preprint arXiv:1904.04751},
year = {2019}
}