loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Aibek Alanov 1 ; 2 ; 3 ; Max Kochurov 2 ; 4 ; Denis Volkhonskiy 4 ; Daniil Yashkov 5 ; Evgeny Burnaev 4 and Dmitry Vetrov 2 ; 3

Affiliations: 1 National Research University Higher School of Economics, Moscow, Russia ; 2 Samsung AI Center Moscow, Moscow, Russia ; 3 Samsung-HSE Laboratory, National Research University Higher School of Economics, Moscow, Russia ; 4 Skolkovo Institute of Science and Technology, Moscow, Russia ; 5 Federal Research Center ”Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russia

Keyword(s): Texture Synthesis, Manifold Learning, Deep Learning, Generative Adversarial Networks.

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.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.227.48.131

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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 (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 214-221. DOI: 10.5220/0008924502140221

@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 (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={214-221},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008924502140221},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - User-controllable Multi-texture Synthesis with Generative Adversarial Networks
SN - 978-989-758-402-2
IS - 2184-4321
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
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic