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CLOTH3D: Clothed 3D Humans

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12365)

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 Human Garment Cloth Dataset Generative model 

Notes

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.

Supplementary material

Supplementary material 1 (mp4 65188 KB)

504476_1_En_21_MOESM2_ESM.pdf (25.4 mb)
Supplementary material 2 (pdf 26045 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universitat de BarcelonaBarcelonaSpain
  2. 2.Computer Vision CenterBarcelonaSpain

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