Skip to main content
Springer Nature Link
Log in

3D Shape-Adapted Garment Generation with Sketches

  • Conference paper
  • First Online:
Advances in Computer Graphics (CGI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13002))

Included in the following conference series:

Abstract

Garment generation or reconstruction is becoming extremely demanding for many digital applications, and the traditional process is time-consuming. In recent years, garment reconstruction from sketch leveraging deep learning and principal component analysis (PCA) has made great progress. In this paper, we present a data-driven approach wherein 3D garments are directly generated from sketches combining given body shape parameters. Our framework is an encoder-decoder architecture. In our network, sketch features extracted by DenseNet and body shape parameters were encoded to latent code respectively. Then, the new latent code obtained by adding two latent codes of the sketch and human body shape is decoded by a fully convolutional mesh decoder. Our network enables the body shape adapted detailed 3D garment generation by leveraging garment sketch and body shape parameters. With the fully convolutional mesh decoder, the network can show the effect of body shape and sketch on the generated garment. Experimental results show that the fully convolutional mesh decoder we used to reconstruct the garment performs higher accuracy and maintains lots of detail compared with the PCA-based method.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Clo - 3d fashion design software (2021). https://www.clo3d.com/

  2. torchvision.models - torchvision master documentation (2021). https://pytorch.org/vision/stable/models.html

  3. Aouaidjia, K., Sheng, B., Li, P., Kim, J., Feng, D.D.: Efficient body motion quantification and similarity evaluation using 3-d joints skeleton coordinates. IEEE Trans. Syst. Man Cybern. Syst. 51(5), 2774–2788 (2021). https://doi.org/10.1109/TSMC.2019.2916896

    Article  Google Scholar 

  4. Bradley, D., Popa, T., Sheffer, A., Heidrich, W., Boubekeur, T.: Markerless garment capture. In: ACM SIGGRAPH 2008 Papers, pp. 1–9 (2008)

    Google Scholar 

  5. Chen, X., Zhou, B., Lu, F.X., Wang, L., Bi, L., Tan, P.: Garment modeling with a depth camera. ACM Trans. Graph. 34(6), 203–1 (2015)

    Google Scholar 

  6. Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)

  7. Danundefinedřek, R., Dibra, E., Öztireli, C., Ziegler, R., Gross, M.: Deepgarment: 3d garment shape estimation from a single image. Comput. Graph. Forum 36(2), 269–280 (2017). https://doi.org/10.1111/cgf.13125

  8. DeCarlo, D., Finkelstein, A., Rusinkiewicz, S., Santella, A.: Suggestive contours for conveying shape. In: ACM SIGGRAPH 2003 Papers, pp. 848–855 (2003)

    Google Scholar 

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

    Article  Google Scholar 

  10. 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: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8739–8748 (2019)

    Google Scholar 

  11. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  12. Igarashi, T., Moscovich, T., Hughes, J.F.: As-rigid-as-possible shape manipulation. ACM Trans. Graph. (TOG) 24(3), 1134–1141 (2005)

    Article  Google Scholar 

  13. Jeong, M.H., Han, D.H., Ko, H.: Garment capture from a photograph. Comput. Animation Virtual Worlds 26, 291–300 (2015)

    Article  Google Scholar 

  14. Jung, A., Hahmann, S., Rohmer, D., Begault, A., Boissieux, L., Cani, M.P.: Sketching folds: developable surfaces from non-planar silhouettes. ACM Trans. Graph. 34(5), November 2015. https://doi.org/10.1145/2749458

  15. Lassner, C., Romero, J., Kiefel, M., Bogo, F., Black, M.J., Gehler, P.V.: Unite the people: Closing the loop between 3d and 2d human representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6050–6059 (2017)

    Google Scholar 

  16. Li, C., Pan, H., Liu, Y., Tong, X., Sheffer, A., Wang, W.: Bendsketch: Modeling freeform surfaces through 2d sketching. ACM Trans. Graph. 36(4), July 2017. https://doi.org/10.1145/3072959.3073632

  17. 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), 1–16 (2015)

    Article  Google Scholar 

  18. Neophytou, A., Hilton, A.: A layered model of human body and garment deformation. In: 2014 2nd International Conference on 3D Vision, vol. 1, pp. 171–178. IEEE (2014)

    Google Scholar 

  19. 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 

  20. Pons-Moll, G., Pujades, S., Hu, S., Black, M.J.: Clothcap: seamless 4d clothing capture and retargeting. ACM Trans. Graph. (TOG) 36(4), 1–15 (2017)

    Article  Google Scholar 

  21. Popa, T., et al.: Wrinkling captured garments using space-time data-driven deformation. In: Computer Graphics Forum, vol. 28, pp. 427–435. Wiley Online Library (2009)

    Google Scholar 

  22. Pritchard, D., Heidrich, W.: Cloth motion capture. In: Computer Graphics Forum, vol. 22, pp. 263–271. Wiley Online Library (2003)

    Google Scholar 

  23. Robson, C., Maharik, R., Sheffer, A., Carr, N.: Context-aware garment modeling from sketches. Comput. Graph. 35(3), 604–613 (2011)

    Article  Google Scholar 

  24. 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 

  25. Scholz, V., Stich, T., Magnor, M., Keckeisen, M., Wacker, M.: Garment motion capture using color-coded patterns. In: ACM SIGGRAPH 2005 Sketches, pp. 38-es (2005)

    Google Scholar 

  26. Tiwari, G., Bhatnagar, B.L., Tung, T., Pons-Moll, G.: Sizer: a dataset and model for parsing 3d clothing and learning size sensitive 3d clothing. arXiv preprint arXiv:2007.11610 (2020)

  27. Wang, H., Hecht, F., Ramamoorthi, R., O’Brien, J.F.: Example-based wrinkle synthesis for clothing animation. In: ACM SIGGRAPH 2010 Papers, pp. 1–8 (2010)

    Google Scholar 

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

    Google Scholar 

  29. White, R., Crane, K., Forsyth, D.A.: Capturing and animating occluded cloth. ACM Trans. Graph. (TOG) b(3), 34-es (2007)

    Google Scholar 

  30. Xu, H., Li, J., Lu, G., Zhang, D., Long, J.: Predicting ready-made garment dressing fit for individuals based on highly reliable examples. Comput. Graph. 90, 135–144 (2020). https://doi.org/10.1016/j.cag.2020.06.002. https://www.sciencedirect.com/science/article/pii/S0097849320300911

  31. Zhou, B., Chen, X., Fu, Q., Guo, K., Tan, P.: Garment modeling from a single image. In: Computer Graphics Forum, vol. 32, pp. 85–91. Wiley Online Library (2013)

    Google Scholar 

  32. Zhou, Y., Wu, C., Li, Z., Cao, C., Ye, Y., Saragih, J., Li, H., Sheikh, Y.: Fully convolutional mesh autoencoder using efficient spatially varying kernels. arXiv preprint arXiv:2006.04325 (2020)

Download references

Acknowledgements

This paper is supported by Natural Science Foundation of Guangdong Province (No. 2021A1515011849, No. 2019A1515011793) and the Fundamental Research Funds for the Central Universities (No. 2020ZYGXZR042).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, China

    Yijing Chen, Chuhua Xian & Guiqing Li

  2. BlueFire AI, Hong Kong, China

    Shuo Jin

Authors
  1. Yijing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuhua Xian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuo Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guiqing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chuhua Xian .

Editor information

Editors and Affiliations

  1. University of Geneva, Carouge, Switzerland

    Nadia Magnenat-Thalmann

  2. University of Minnesota, Minneapolis, MN, USA

    Victoria Interrante

  3. EPFL, Lausanne, Switzerland

    Daniel Thalmann

  4. University of Crete, Heraklion, Crete, Greece

    George Papagiannakis

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  7. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, Y., Xian, C., Jin, S., Li, G. (2021). 3D Shape-Adapted Garment Generation with Sketches. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-89029-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89028-5

  • Online ISBN: 978-3-030-89029-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics