Skip to main content
Book cover

European Conference on Computer Vision

ECCV 2020: Computer Vision – ECCV 2020 pp 344–359Cite as

CLOTH3D: Clothed 3D Humans

Part of the Lecture Notes in Computer Science book series (LNIP,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

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-58565-5_21
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-58565-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Notes

  1. 1.

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

References

  1. Carnegie-Mellon Mocap Database. http://mocap.cs.cmu.edu/

  2. Alldieck, T., Magnor, M., Xu, W., Theobalt, C., Pons-Moll, G.: Detailed human avatars from monocular video. In: 2018 International Conference on 3D Vision (3DV), pp. 98–109. IEEE (2018)

    Google Scholar 

  3. Alldieck, T., Pons-Moll, G., Theobalt, C., Magnor, M.: Tex2Shape: detailed full human body geometry from a single image. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2293–2303 (2019)

    Google Scholar 

  4. Amberg, B., Romdhani, S., Vetter, T.: Optimal step nonrigid ICP algorithms for surface registration. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

  5. Bhatnagar, B.L., Tiwari, G., Theobalt, C., Pons-Moll, G.: Multi-garment net: learning to dress 3D people from images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5420–5430 (2019)

    Google Scholar 

  6. Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond euclidean data. IEEE Sig. Process. Mag. 34(4), 18–42 (2017)

    CrossRef  Google Scholar 

  7. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in neural information processing systems, pp. 3844–3852 (2016)

    Google Scholar 

  8. Dong, Q., Gong, S., Zhu, X.: Multi-task curriculum transfer deep learning of clothing attributes. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 520–529. IEEE (2017)

    Google Scholar 

  9. Garland, M., Heckbert, P.S.: Surface simplification using quadric error metrics. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 209–216. ACM Press/Addison-Wesley Publishing Co. (1997)

    Google Scholar 

  10. Guan, P., Reiss, L., Hirshberg, D.A., Weiss, A., Black, M.J.: Drape: dressing any person. ACM Trans. Graph. 31(4), 35:1–35:10 (2012)

    CrossRef  Google Scholar 

  11. Gundogdu, E., Constantin, V., Seifoddini, A., Dang, M., Salzmann, M., Fua, P.: GarNet: a two-stream network for fast and accurate 3D cloth draping. In: IEEE International Conference on Computer Vision (ICCV). IEEE, October 2019

    Google Scholar 

  12. Lahner, Z., Cremers, D., Tung, T.: DeepWrinkles: accurate and realistic clothing modeling. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 667–684 (2018)

    Google Scholar 

  13. Lin, K., Yang, H.F., Liu, K.H., Hsiao, J.H., Chen, C.S.: Rapid clothing retrieval via deep learning of binary codes and hierarchical search. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 499–502. ACM (2015)

    Google Scholar 

  14. Loper, M., Mahmood, N., Black, M.J.: Mosh: motion and shape capture from sparse markers. ACM Trans. Graph. (TOG) 33(6), 220 (2014)

    CrossRef  Google Scholar 

  15. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 34(6), 248:1–248:16 (2015)

    Google Scholar 

  16. Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (TOG) 34(6), 248 (2015)

    CrossRef  Google Scholar 

  17. Ma, Q., Tang, S., Pujades, S., Pons-Moll, G., Ranjan, A., Black, M.J.: Dressing 3D humans using a conditional Mesh-VAE-GAN. arXiv preprint arXiv:1907.13615 (2019)

  18. von Marcard, T., Henschel, R., Black, M., Rosenhahn, B., Pons-Moll, G.: Recovering accurate 3D human pose in the wild using IMUs and a moving camera. In: European Conference on Computer Vision (ECCV), September 2018

    Google Scholar 

  19. Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: International Conference on Machine Learning, pp. 2014–2023 (2016)

    Google Scholar 

  20. Nikolenko, S.I.: Synthetic data for deep learning. arXiv abs/1909.11512 (2019)

    Google Scholar 

  21. Patel, C., Liao, Z., Pons-Moll, G.: TailorNet: predicting clothing in 3D as a function of human pose, shape and garment style. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7365–7375 (2020)

    Google Scholar 

  22. Pons-Moll, G., Pujades, S., Hu, S., Black, M.J.: ClothCap: seamless 4D clothing capture and retargeting. ACM Trans. Graph. (TOG) 36(4), 73 (2017)

    CrossRef  Google Scholar 

  23. Pumarola, A., Goswami, V., Vicente, F., De la Torre, F., Moreno-Noguer, F.: Unsupervised image-to-video clothing transfer. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, October 2019

    Google Scholar 

  24. Pumarola, A., Sanchez-Riera, J., Choi, G., Sanfeliu, A., Moreno-Noguer, F.: 3Dpeople: modeling the geometry of dressed humans. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2242–2251 (2019)

    Google Scholar 

  25. Ros, G., Sellart, L., Materzynska, J., Vazquez, D., Lopez, A.M.: The SYNTHIA dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

    Google Scholar 

  26. Santesteban, I., Otaduy, M.A., Casas, D.: Learning-based animation of clothing for virtual try-on. In: Computer Graphics Forum, vol. 38, pp. 355–366. Wiley Online Library (2019)

    Google Scholar 

  27. Shin, D., Chen, Y.: Deep garment image matting for a virtual try-on system. In: The IEEE International Conference on Computer Vision (ICCV) Workshops, October 2019

    Google Scholar 

  28. Varol, G., et al.: Learning from synthetic humans. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 109–117 (2017)

    Google Scholar 

  29. Wang, T.Y., Ceylan, D., Popovic, J., Mitra, N.J.: Learning a shared shape space for multimodal garment design. arXiv preprint arXiv:1806.11335 (2018)

  30. Wang, T.Y., Shao, T., Fu, K., Mitra, N.J.: Learning an intrinsic garment space for interactive authoring of garment animation. ACM Trans. Graph. (TOG) 38(6), 1–12 (2019)

    Google Scholar 

  31. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. arXiv preprint arXiv:1901.00596 (2019)

  32. Yang, J., Franco, J.S., Hétroy-Wheeler, F., Wuhrer, S.: Analyzing clothing layer deformation statistics of 3D human motions. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 237–253 (2018)

    Google Scholar 

  33. Yu, T., et al.: SimulCap: single-view human performance capture with cloth simulation. arXiv preprint arXiv:1903.06323 (2019)

  34. Yuan, Y.J., Lai, Y.K., Yang, J., Fu, H., Gao, L.: Mesh variational autoencoders with edge contraction pooling. arXiv preprint arXiv:1908.02507 (2019)

  35. Zhang, C., Pujades, S., Black, M.J., Pons-Moll, G.: Detailed, accurate, human shape estimation from clothed 3D scan sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4191–4200 (2017)

    Google Scholar 

Download references

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.

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meysam Madadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sergio Escalera
    View author publications

    You can also search for this author in PubMed Google Scholar

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

    Prof. Dr. Thomas Brox

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

    Jan-Michael Frahm

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 65188 KB)

Supplementary material 2 (pdf 26045 KB)

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58564-8

  • Online ISBN: 978-3-030-58565-5

  • eBook Packages: Computer ScienceComputer Science (R0)