Advertisement

3D Body Modelling and Applications

  • S. Alemany
  • A. Ballester
  • E. Parrilla
  • A. Pierola
  • J. Uriel
  • B. Nacher
  • A. Remon
  • A. Ruescas
  • J. V. Durá
  • P. Piqueras
  • C. Solves
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 826)

Abstract

Human body metrics have become a significant source of product innovation to industries where consumer fit, comfort and ergonomic considerations are key factors. This is especially the case for fashion (e.g. footwear or apparel), health (e.g. orthotics or prosthetics), transport and aerospace (e.g. seats or human-machine interfaces), and safety (e.g. protective equipment or workstations) among others. Large-scale databases of 3D body scans are today a research tool for most of the leading companies of those sectors.

In the last few years, new emerging businesses using 3D body data (e.g. garment and footwear customization, size recommendation, health monitoring) are increasing the number and size of 3D body scan repositories. 3D body databases are growing very fast and the development of 3D modelling tools is leveraging the practical application and exploitation of these data.

This paper presents three applications of 3D body modelling methods based on Principal Component Analysis (PCA): (1) shape analysis applied to the ergonomic sizing and design of products, (2) creation of 3D avatars from body measurements, and (3) serial 3D creation of harmonised watertight meshes acquired with any type of 3D body scanner.

Keywords

3D human models Body avatars 3D mannequins Anthropometry 

Notes

Acknowledgments

The authors thank the European Commission, the Instituto Valenciano de Competitividad Empresarial (IVACE) and the Agencia Estatal de Investigación del Ministerio de Economía, Industria y Competiti-vidad (MINECO) for the financial support of this research though the following projects: In-Kreate (funded by the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 731885), BodyPass (funded by the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement no. 779780), 3DBODY_HUB (submitted to IVACE with a funding of Generalitat Valenciana and the European Regional Development Fund and the proposal nº IMDEEA/2018/49) and Torres Quevedo (funded by MINECO under the program Torres Quevedo 2016).

References

  1. 1.
    Pheasant S (1991) Ergonomics, work and health. Palgrave, BasingstokeCrossRefGoogle Scholar
  2. 2.
    Duffy VG (2016) Handbook of digital human modeling: research for applied ergonomics and human factors engineering. CRC Press, Boca RatonGoogle Scholar
  3. 3.
    Reed MP et al (2014) Developing and implementing parametric human body shape models in ergonomics software. In: Proceedings of the 3rd international digital human modeling conference, TokyoGoogle Scholar
  4. 4.
    Robinette, KM, Daanen H, Paquet E (1999) The CAESAR project: a 3-D surface anthropometry survey. In: Proceedings of the second international conference on 3-D digital imaging and modeling, IEEE (1999)Google Scholar
  5. 5.
    Ballester A et al (2015) 3D body databases of the spanish population and its application to the apparel industry. In: Proceedings of 6th international conference on 3D body scanning technologies, Lugano, SwitzerlandGoogle Scholar
  6. 6.
    Trieb R et al (2013) EUROFIT—integration, homogenisation and extension of the scope of large 3D anthropometric data pools for product development. In: 4th International conference and exhibition on 3D body scanning technologies, Long Beach, CA, USA (2013)Google Scholar
  7. 7.
    Allen B, Curless B, Popović Z (2003) The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans Graphics (TOG) 22(3):587–594CrossRefGoogle Scholar
  8. 8.
    Reed MP, Park BKD (2017) Comparison of boundary manikin generation methods. In: 5th International digital human modeling symposiumGoogle Scholar
  9. 9.
    Zeng Y, Fu J, Chao H (2017) 3D human body reshaping with anthropometric modeling. In: International conference on internet multimedia computing and service. Springer, SingaporeGoogle Scholar
  10. 10.
    Reed MP et al (2014) Developing and implementing parametric human body shape models in ergonomics software. In: Proceedings of the 3rd international digital human modeling conference, TokyoGoogle Scholar
  11. 11.
    Wuhrer S, Shu C (2013) Estimating 3D human shapes from measurements. Mach Vis Appl 24(6):1133–1147CrossRefGoogle Scholar
  12. 12.
    Koo B-Y et al (2015) Example-based statistical framework for parametric modeling of human body shapes. Comput Ind 73:23–38CrossRefGoogle Scholar
  13. 13.
    Baek S-Y, Lee K (2012) Parametric human body shape modeling framework for human-centered product design. Comput Aided Des 44(1):56–67CrossRefGoogle Scholar
  14. 14.
    Seo H, Yeo YI, Wohn K (2006) 3D body reconstruction from photos based on range scan. In: International conference on technologies for e-learning and digital entertainment. Springer, Heidelberg (2006)Google Scholar
  15. 15.
    Xi P, Lee W-S, Shu C (2007) A data-driven approach to human-body cloning using a segmented body database. In: 15th pacific conference on computer graphics and applications. PG 2007. IEEEGoogle Scholar
  16. 16.
    Zhu S, Mok PY, Kwok YL (2013) An efficient human model customization method based on orthogonal-view monocular photos. Comput Aided Des 45(11):1314–1332CrossRefGoogle Scholar
  17. 17.
    Saito S et al (2014) Model-based 3D human shape estimation from silhouettes for virtual fitting. In: Three-dimensional image processing, measurement (3DIPM), and applications 2014, vol 9013. International Society for Optics and PhotonicsGoogle Scholar
  18. 18.
    Mok PY, Zhu S (2018) Precise shape estimation of dressed subjects from two-view image sets. In: Applications of computer vision in fashion and textiles, pp 273–292Google Scholar
  19. 19.
    Ballester A et al (2016) Data-driven three-dimensional reconstruction of human bodies using a mobile phone app. Int J Digital Hum 1(4):361–388CrossRefGoogle Scholar
  20. 20.
    Weiss, A, Hirshberg D, Black MJ (2011) Home 3D body scans from noisy image and range data. In: 2011 IEEE international conference on computer vision (ICCV). IEEEGoogle Scholar
  21. 21.
    Lu Y et al. Accurate nonrigid 3D human body surface reconstruction using commodity depth sensors. Comput Animation Virt Worlds e1807Google Scholar
  22. 22.
    Park B-K, Reed MP (2014) Rapid generation of custom avatars using depth cameras. In: Proceedings of the 3rd international digital human modeling conferenceGoogle Scholar
  23. 23.
    Tong J et al (2012) Scanning 3D full human bodies using kinects. IEEE Trans Vis Comput Graphics 18(4):643–650CrossRefGoogle Scholar
  24. 24.
    Allen B et al (2006) Learning a correlated model of identity and pose-dependent body shape variation for real-time synthesis. In: Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on computer animation. Eurographics AssociationGoogle Scholar
  25. 25.
    Anguelov D et al (2005) SCAPE: shape completion and animation of people. In: ACM transactions on graphics (TOG), vol 24, no 3. ACM (2005)Google Scholar
  26. 26.
    Hasler N et al (2009) A statistical model of human pose and body shape. In: Computer graphics forum, vol 28, no 2. Blackwell Publishing LtdGoogle Scholar
  27. 27.
    Hirshberg DA et al (2012) Coregistration: simultaneous alignment and modeling of articulated 3D shape. In: European conference on computer vision. Springer, HeidelbergGoogle Scholar
  28. 28.
    Istook CL, Hwang S-J (2001) 3D body scanning systems with application to the apparel industry. J Fashion Mark Manag Int J 5(2):120–132CrossRefGoogle Scholar
  29. 29.
    D’Apuzzo N, Gruen A (2009) Recent advances in 3D full body scanning with applications to fashion and apparel. Optical 3-D measurement techniques IX (2009)Google Scholar
  30. 30.
    Treleaven P, Wells J (2007) 3D body scanning and healthcare applications. Computer 40(7):28–34CrossRefGoogle Scholar
  31. 31.
    Alemany S, González JC, Nácher B, Soriano C, Arnáiz C, Heras H (2010) Anthropometric survey of the Spanish female population aimed at the apparel industry. In: Proceedings of the 2010 international conference on 3D body scanning technologies. Lugano, SwitzerlandGoogle Scholar
  32. 32.
    Amberg B, Romdhani S, Vetter T (2007 June) Optimal step nonrigid ICP algorithms for surface registration. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR 2007. IEEE, pp 1–8Google Scholar
  33. 33.
    Sumner RW, Popović J (2004, August). Deformation transfer for triangle meshes. In: ACM Transactions on graphics (TOG), vol 23, no 3, pp 399–405. ACMGoogle Scholar
  34. 34.
    Gower JC (1975) Generalized procrustes analysis. Psychometrika 40(1):33–51MathSciNetCrossRefGoogle Scholar
  35. 35.
    Zehner GF, Meindl RS, Hudson JA (1993) A multivariate anthropometric method for crew station design. Kent State University oHGoogle Scholar
  36. 36.
    Robinette KM, McConville JT (1981) An alternative to percentile models (No 810217). SAE technical paperGoogle Scholar
  37. 37.
    Robinette KM (1998) Multivariate methods in engineering anthropometry. In: Proceedings of the human factors and ergonomics society annual meeting vol 42, no 10. SAGE Publications, Sage, Los AngelesGoogle Scholar
  38. 38.
    Lacko D et al (2017) Product sizing with 3D anthropometry and k-medoids clustering. Comput Aided Des 91:60–74CrossRefGoogle Scholar
  39. 39.
    Lee W et al (2016) Application of massive 3D head and facial scan datasets in ergonomic head-product design. Int J Digital Hum 1(4):344–360CrossRefGoogle Scholar
  40. 40.
    Veitch D, Veitch L, Henneberg M (2007) Sizing for the clothing industry using principal component analysis—an Australian example. J ASTM Int 4(3):1–12Google Scholar
  41. 41.
    Durá JV, Caprara G, Ballester A, Pierola A, Kozomara Z (2018) Preliminary results of the InKreate Project. Revista de Biomecánica 65Google Scholar
  42. 42.
    Han H, Nam Y, Choi K (2010) Comparative analysis of 3D body scan measurements and manual measurements of size Korea adult females. Int J Ind Ergon 40(5):530–540CrossRefGoogle Scholar
  43. 43.
    Markiewicz Ł et al (2017) 3D anthropometric algorithms for the estimation of measurements required for specialized garment design. Expert Syst Appl 85:366–385CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Instituto de Biomecánica de ValenciaUniversitat Politècnica de ValenciaValenciaSpain

Personalised recommendations