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CLOTH3D: Clothed 3D Humans

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Computer Vision – ECCV 2020 (ECCV 2020)

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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.

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Notes

  1. 1.

    We include gender as an additional dimension to the shape parameters.

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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.

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Authors and Affiliations

  1. Universitat de Barcelona, Barcelona, Spain

    Hugo Bertiche, Meysam Madadi & Sergio Escalera

  2. Computer Vision Center, Barcelona, Spain

    Hugo Bertiche, Meysam Madadi & Sergio Escalera

Authors
  1. Hugo Bertiche
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  2. Meysam Madadi
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  3. Sergio Escalera
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Corresponding author

Correspondence to Hugo Bertiche .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Bertiche, H., Madadi, M., Escalera, S. (2020). CLOTH3D: Clothed 3D Humans. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12365. Springer, Cham. https://doi.org/10.1007/978-3-030-58565-5_21

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  • DOI: https://doi.org/10.1007/978-3-030-58565-5_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-58565-5

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