3D SfM as a Measuring Technique for Human Body Transformation

  • Alessandro MarroEmail author
  • Stefan Wiesen
  • Max Langbein
  • Hans Hagen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


The tracking of fat loss as well as muscle gain has always been one of the most important steps during a person’s fitness journey. It does not only motivate to continue practicing exercises, but also helps to develop specific workout plans to enhance particular body parts of athletes. Structure for Motion (SfM), unlike other reconstruction techniques, produces acceptable results from low-quality inputs. This makes the method applicable for ubiquitous equipment like a smartphone camera, while still being scalable to professional environments with proper equipment. In order to track overall body transformation, we propose a photogrammetry workflow employing SfM, reproducibly generating a model of the human body in different stages of a fitness plan. For visualization, we do a mesh alignment step followed by a comparison between the reconstructed body models of the subject, resulting in color-mapped meshes. Following this workflow the transformation of specific body regions can be analyzed in detail, only using consumer hardware.


Body transformation SfM Photogrammetry 


  1. 1.
    Cignoni, P., Callieri, M., Corsini, M., Dellepiane, M., Ganovelli, F., Ranzuglia, G.: MeshLab: an open-source mesh processing tool. In: Eurographics Italian Chapter Conference (2008)Google Scholar
  2. 2.
    Schaffalitzky, F., Zisserman, A.: Multi-view matching for unordered image sets, or “How Do I Organize My Holiday Snaps?”. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 414–431. Springer, Heidelberg (2002). Scholar
  3. 3.
    Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4104–4113 (2016)Google Scholar
  4. 4.
    Shaw, M.P., Robinson, J., Peart, D.J.: Comparison of a mobile application to estimate percentage body fat to other non-laboratory based measurements. Biomed. Hum. Kinet. 9(1), 94–98 (2017)CrossRefGoogle Scholar
  5. 5.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. ACM Trans. Graph. (TOG) 25, 835–846 (2006)CrossRefGoogle Scholar
  6. 6.
    Wang, J., Gallagher, D., Thornton, J.C., Yu, W., Horlick, M., Pi-Sunyer, F.X.: Validation of a 3-dimensional photonic scanner for the measurement of body volumes, dimensions, and percentage body fat–. Am. J. Clin. Nutr. 83(4), 809–816 (2006)CrossRefGoogle Scholar
  7. 7.
    Wells, J., Fewtrell, M.: Measuring body composition. Arch. Dis. Childhood 91(7), 612–617 (2006)CrossRefGoogle Scholar
  8. 8.
    Wells, J., Ruto, A., Treleaven, P.: Whole-body three-dimensional photonic scanning: a new technique for obesity research and clinical practice. Int. J. Obes. 32(2), 232 (2008)CrossRefGoogle Scholar
  9. 9.
    Wells, J.C., et al.: Acceptability, precision and accuracy of 3D photonic scanning for measurement of body shape in a multi-ethnic sample of children aged 5–11 years: the slic study. PLoS One 10(4), e0124193 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alessandro Marro
    • 1
    Email author
  • Stefan Wiesen
    • 1
  • Max Langbein
    • 1
  • Hans Hagen
    • 1
  1. 1.TU KaiserslauternKaiserslauternGermany

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