Skip to main content

Advertisement

Log in

Human body construction based on combination of parametric and nonparametric reconstruction methods

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Nowadays, 3D human models are widely used in the garment industry where it is important to reconstruct compliant human model from scan of the body under partial dress for privacy reasons. A new 3D human construction method based on the combination of parametric and nonparametric reconstruction is proposed here. Inputs to the method include the raw scan and partial anthropometric parameters. The scan is divided into exposed area and clothing occluded area for modeling separately. The information of the clothing occluded area is restored by semantic parametric human modeling and pose fitting. The information of the exposed area is restored by the method of non-rigid registration. Then, the two types of information are fused to reconstruct the final human model. The experiment divided the human scan into three common situations: naked, partially clothed and highly clothed body scanning. The results of the qualitative and quantitative analyses show that the method is able to fit the parametric human model to the exposed area scan while matching the user input on the anthropometric parameters in the clothing occluded area. It is worth pointing out that the connection between the two areas is smooth. The performance is also better than previous related methods. The proposed method reduces the dressing requirements for reconstructing the human body from 3D scan and also demonstrates the validity, accuracy and versatility of the method for reconstructing human model.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Chen, D., et al.: 3D human body reconstruction based on SMPL model. The Vis. Comput. 39(5), 1893–1906 (2022)

    Article  Google Scholar 

  2. Hasler, N., et al.: Estimating body shape of dressed humans. Comput. Graph. 33(3), 211–216 (2009)

    Article  Google Scholar 

  3. Song, D., et al.: A Semantic Parametric Model for 3D Human Body Reshaping. Springer, Cham (2018)

    Google Scholar 

  4. Zeng, Y., Fu, J. and Chao, H.: 3D Human body reshaping with anthropometric modeling. In: International Conference on Internet Multimedia Computing and Service, pp. 96–107. Springer Singapore, Singapore (2017)

    Google Scholar 

  5. Hu, P., et al.: Personalized 3D mannequin reconstruction based on 3D scanning. Int. J. Cloth. Sci. Technol. 30(2), 159–174 (2018)

    Article  Google Scholar 

  6. Haoyang, et al.: Structure-consistent customized virtual mannequin reconstruction from 3D scans based on optimization. Text. Res. J. 90(7–8), 937–950 (2019)

    Google Scholar 

  7. Li, X., et al.: Design of a multi-sensor information acquisition system for mannequin reconstruction and human body size measurement under clothes. Text. Res. J. (2022). https://doi.org/10.1177/00405175221093663

    Article  Google Scholar 

  8. Anguelov, D., et al.: SCAPE: shape completion and animation of people. ACM Trans. Graph. 24(3), 408–416 (2005)

    Article  Google Scholar 

  9. Loper, M., et al.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (TOG) 34(6), 1–16 (2015)

    Article  Google Scholar 

  10. Romero, J., Tzionas, D. and Black, M. J.: Embodied hands: modeling and capturing hands and bodies together. arXiv preprint arXiv:2201.02610, 2022

  11. Pavlakos, G. et al.: Expressive body capture: 3d hands, face, and body from a single image. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.

  12. Osman, A. A., Bolkart, T. and Black, M. J.: Star: sparse trained articulated human body regressor. In: European Conference on Computer Vision. 2020. Springer

  13. Wang, Y., et al.: PanoMan: sparse localized components–based model for full human motions. ACM Trans. Graph. (TOG) 40(2), 1–17 (2021)

    Google Scholar 

  14. Alldieck, T., Xu, H. and Sminchisescu, C.: Imghum: implicit generative models of 3d human shape and articulated pose. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021

  15. Joo, H., Simon, T. and Sheikh, Y.: Total capture: a 3d deformation model for tracking faces, hands, and bodies. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018

  16. Yang, Y. et al.: Semantic parametric reshaping of human body models. In: 2014 2nd International Conference on 3D Vision (3DV), 2014

  17. Li, X., et al.: Remodeling of mannequins based on automatic binding of mesh to anthropometric parameters. The Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02738-1

    Article  Google Scholar 

  18. Chen, G., et al.: Reconstructing 3D human models with a Kinect. Comput. Anim. Virt. Worlds 27(1), 72–85 (2016)

    Article  Google Scholar 

  19. Bogo, F. et al.: Keep it SMPL: automatic estimation of 3D human pose and shape from a single image. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, Oct 11–14, 2016, Proceedings, Part V 14. 2016. Springer

  20. Muller, L. et al.: On self-contact and human pose. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021

  21. Omran, M. et al.: Neural body fitting: unifying deep learning and model-based human pose and shape estimation. In: International Conference on 3d Vision, 2018

  22. Choutas, V. et al.: Monocular expressive body regression through body-driven attention. In: European Conference on Computer Vision, 2020. Springer

  23. Yu, T. et al.: DoubleFusion: real-time capture of human performances with inner body shapes from a single depth sensor. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

  24. Kolotouros, N. et al.: Learning to reconstruct 3D human pose and shape via model-fitting in the loop. In: Proceedings of the IEEE/CVF international conference on computer vision, 2019

  25. Izadi, S. et al.: KinectFusion: real-time dynamic 3D surface reconstruction and interaction. In: Acm Siggraph. 2011

  26. Wang, R., et al.: Capturing dynamic textured surfaces of moving targets. In: European Conference on Computer Vision. 2016. Springer

  27. Tong, J., et al.: Scanning 3D full human bodies using kinects. IEEE Trans. Vis. Comput. Graph. 18(4), 643–650 (2012)

    Article  Google Scholar 

  28. Yan, S., Wirta, J., and Kämäräinen, J. K.: Anthropometric clothing measurements from 3D body scans. Mach. Vis. Appl. 31(1–2), 7 (2020)

    Article  Google Scholar 

  29. Blackwell, S. et al.: Civilian American and European surface anthropometry resource (CAESAR). Volume 2: Descriptions. 2002, SYTRONICS INC DAYTON OH

  30. Saito S. et al.: Pifu: pixel-aligned implicit function for high-resolution clothed human digitization. In: Proceedings of the IEEE/CVF international conference on computer vision, 2019

  31. Xiu, Y. et al.: ICON: implicit clothed humans obtained from normals. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. IEEE

  32. Chibane, J., Alldieck, T. and Pons-Moll, G.: Implicit functions in feature space for 3d shape reconstruction and completion. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020.

  33. Bhatnagar, B.L. et al.: Combining implicit function learning and parametric models for 3d human reconstruction. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, Aug 23–28, 2020, Proceedings, Part II 16. 2020. Springer

  34. Bogo, F. et al.: FAUST: Dataset and evaluation for 3D mesh registration. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2014

  35. Sumner, R.W., Popović, J.: Deformation transfer for triangle meshes. ACM Trans. Graph. (TOG) 23(3), 399–405 (2004)

    Article  Google Scholar 

  36. Alldieck, T. et al.: Learning to reconstruct people in clothing from a single RGB camera. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019

  37. Lazova, V., Insafutdinov, E. and Pons-Moll, G.: 360-degree textures of people in clothing from a single image. In: 2019 International Conference on 3D Vision (3DV), 2019. IEEE

  38. Cao, Z. et al.: Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017

  39. Yao, Y. et al.: Quasi-Newton solver for robust non-rigid registration. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020

  40. Sorkine, O. and Alexa, M.: As-rigid-as-possible surface modeling. In: Symposium on Geometry processing, 2007

  41. Yang, L. et al.: Renovating parsing R-CNN for accurate multiple human parsing. In: Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, Aug 23–28, 2020, Proceedings, PartXII. Springer (2020)

  42. Zheng, Z., et al.: Pamir: parametric model-conditioned implicit representation for imagebased human reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3170–3184 (2021)

    Article  Google Scholar 

  43. Zhang, C., et al.: Detailed, accurate, human shape estimation from clothed 3D scan sequences. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2017)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guiqin Li.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Li, G., Li, T. et al. Human body construction based on combination of parametric and nonparametric reconstruction methods. Vis Comput 40, 5557–5573 (2024). https://doi.org/10.1007/s00371-023-03122-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-023-03122-3

Keywords

Navigation