User-controllable Multi-texture Synthesis with Generative Adversarial Networks

Aibek Alanov, Max Kochurov, Denis Volkhonskiy, Daniil Yashkov, Evgeny Burnaev, Dmitry Vetrov

Abstract

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. We show our unsupervised learning pipeline may help segmentation models. Moreover, we apply our method to produce 3D textures and show that it outperforms existing baselines.

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Paper Citation


in Harvard Style

Alanov A., Kochurov M., Volkhonskiy D., Yashkov D., Burnaev E. and Vetrov D. (2020). User-controllable Multi-texture Synthesis with Generative Adversarial Networks.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-402-2, pages 214-221. DOI: 10.5220/0008924502140221


in Bibtex Style

@conference{visapp20,
author={Aibek Alanov and Max Kochurov and Denis Volkhonskiy and Daniil Yashkov and Evgeny Burnaev and Dmitry Vetrov},
title={User-controllable Multi-texture Synthesis with Generative Adversarial Networks},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2020},
pages={214-221},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008924502140221},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - User-controllable Multi-texture Synthesis with Generative Adversarial Networks
SN - 978-989-758-402-2
AU - Alanov A.
AU - Kochurov M.
AU - Volkhonskiy D.
AU - Yashkov D.
AU - Burnaev E.
AU - Vetrov D.
PY - 2020
SP - 214
EP - 221
DO - 10.5220/0008924502140221