CLOTH3D: Clothed 3D Humans

Hugo Bertiche; Meysam Madadi; Sergio Escalera

doi:10.1007/978-3-030-58565-5_21

CLOTH3D: Clothed 3D Humans

Hugo Bertiche^*, Meysam Madadi, Sergio Escalera

^*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

57 Citationer (Scopus)

Abstract

We present CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape.

Originalsprog	Engelsk
Titel	Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings
Redaktører	Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
Antal sider	16
Forlag	Springer
Publikationsdato	2020
Sider	344-359
ISBN (Trykt)	9783030585648
DOI	https://doi.org/10.1007/978-3-030-58565-5_21
Status	Udgivet - 2020
Begivenhed	16th European Conference on Computer Vision, ECCV 2020 - Glasgow, Storbritannien Varighed: 23 aug. 2020 → 28 aug. 2020

Konference

Konference	16th European Conference on Computer Vision, ECCV 2020
Land/Område	Storbritannien
By	Glasgow
Periode	23/08/2020 → 28/08/2020

Navn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind	12365 LNCS
ISSN	0302-9743

Bibliografisk note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

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10.1007/978-3-030-58565-5_21

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Citationsformater

Bertiche, Hugo ; Madadi, Meysam ; Escalera, Sergio. / CLOTH3D : Clothed 3D Humans. Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings. red. / Andrea Vedaldi ; Horst Bischof ; Thomas Brox ; Jan-Michael Frahm. Springer, 2020. s. 344-359 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12365 LNCS).

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title = "CLOTH3D: Clothed 3D Humans",

abstract = "We present CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape.",

keywords = "3D, Cloth, Dataset, Garment, Generative model, Human",

author = "Hugo Bertiche and Meysam Madadi and Sergio Escalera",

note = "Funding Information: Acknowledgments. This work is partially supported by ICREA under the ICREA Academia programme, and by the Spanish project PID2019-105093GB-I00 (MINECO / FEDER, UE) and CERCA Programme / Generalitat de Catalunya. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",

year = "2020",

doi = "10.1007/978-3-030-58565-5_21",

language = "English",

isbn = "9783030585648",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

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booktitle = "Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings",

address = "Germany",

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Bertiche, H, Madadi, M & Escalera, S 2020, CLOTH3D: Clothed 3D Humans. i A Vedaldi, H Bischof, T Brox & J-M Frahm (red), Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 12365 LNCS, s. 344-359, 16th European Conference on Computer Vision, ECCV 2020, Glasgow, Storbritannien, 23/08/2020. https://doi.org/10.1007/978-3-030-58565-5_21

CLOTH3D: Clothed 3D Humans. / Bertiche, Hugo; Madadi, Meysam; Escalera, Sergio.
Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings. red. / Andrea Vedaldi; Horst Bischof; Thomas Brox; Jan-Michael Frahm. Springer, 2020. s. 344-359 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12365 LNCS).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - CLOTH3D

T2 - 16th European Conference on Computer Vision, ECCV 2020

AU - Bertiche, Hugo

AU - Madadi, Meysam

AU - Escalera, Sergio

N1 - Funding Information: Acknowledgments. This work is partially supported by ICREA under the ICREA Academia programme, and by the Spanish project PID2019-105093GB-I00 (MINECO / FEDER, UE) and CERCA Programme / Generalitat de Catalunya. Publisher Copyright: © 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - We present CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape.

AB - We present CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape.

KW - 3D

KW - Cloth

KW - Dataset

KW - Garment

KW - Generative model

KW - Human

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U2 - 10.1007/978-3-030-58565-5_21

DO - 10.1007/978-3-030-58565-5_21

M3 - Article in proceeding

AN - SCOPUS:85097411513

SN - 9783030585648

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 359

BT - Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings

A2 - Vedaldi, Andrea

A2 - Bischof, Horst

A2 - Brox, Thomas

A2 - Frahm, Jan-Michael

PB - Springer

Y2 - 23 August 2020 through 28 August 2020

ER -

Bertiche H, Madadi M, Escalera S. CLOTH3D: Clothed 3D Humans. I Vedaldi A, Bischof H, Brox T, Frahm JM, red., Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings. Springer. 2020. s. 344-359. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 12365 LNCS). doi: 10.1007/978-3-030-58565-5_21

CLOTH3D: Clothed 3D Humans

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