Machine Vision and Applications

, Volume 23, Issue 4, pp 821–830 | Cite as

Human shape correspondence with automatically predicted landmarks

  • Stefanie Wuhrer
  • Pengcheng Xi
  • Chang Shu
Short Paper


We consider the problem of computing accurate point-to-point correspondences among a set of human bodies in similar posture using a landmark-free approach. The approach learns the locations of the anthropometric landmarks present in a database of human models in similar postures and uses this knowledge to automatically predict the locations of these anthropometric landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. This study conducts a large-scale evaluation to examine the accuracy of the computed correspondences. Furthermore, we show that the correspondences are accurate enough for the application of motion transfer.


Shape correspondence Anthropometric landmarks Human models Landmark prediction 


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  1. 1.
    Aiger, D., Mitra, N., Cohen-Or, D.: 4-points congruent sets for robust surface registration. ACM Trans. Graphic. 27(3), #85, 1–10 (2008). In: Proceedings of SIGGRAPHGoogle Scholar
  2. 2.
    Allen, B., Curless, B., Popović, Z.: The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans. Graphic. 22(3), 587–594 (2003). In: Proceedings of SIGGRAPHGoogle Scholar
  3. 3.
    Amberg, B., Romdhani, S., Vetter, T.: Optimal step nonrigid icp algorithms for surface registration. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  4. 4.
    Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J.: Scape: shape completion and animation of people. ACM Trans. Graphic. 24(3), 408–416 (2005). In: Proceedings of SIGGRAPHGoogle Scholar
  5. 5.
    Arya, S., Mount, D.M.: Approximate nearest neighbor queries in fixed dimensions. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 271–280 (1993)Google Scholar
  6. 6.
    Azouz, Z.B., Shu, C., Mantel, A.: Automatic locating of anthropometric landmarks on 3d human models. In: 3D Data Processing, Visualization and Transmission (2006)Google Scholar
  7. 7.
    Baran, I., Popović, J.: Automatic rigging and animation of 3D characters. ACM Trans. Graphic. 26(3), (2007). In: Proceedings of SIGGRAPHGoogle Scholar
  8. 8.
    Besl P., McKay N.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach Intell. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  9. 9.
    Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: Proceedings of SIGGRAPH, pp. 187–194 (1999)Google Scholar
  10. 10.
    Chang W., Zwicker M.: Automatic registration for articulated shapes. Comput. Graphic. Forum (Special Issue of SGP 2008) 27(5), 1459–1468 (2006)CrossRefGoogle Scholar
  11. 11.
    Chang, W., Zwicker, M.: Range scan registration using reduced deformable models. Comput. Graphic. Forum (Special Issue of Eurographics 2009). (To appear)Google Scholar
  12. 12.
    Davies, R.H., Twining, C.J., Cootes, T.F., Waterton, J.C., Taylor, C.J.: 3D statistical shape models using direct optimization of description length. In: Proceedings European Conference Computer Vision (2002)Google Scholar
  13. 13.
    Dryden I., Mardia K.: Statistical Shape Analysis. Wiley, New York (2002)Google Scholar
  14. 14.
    Gall, J., Stoll, C., de Aguiar, E., Theobalt, C., Rosenhahn, B., Seidel, H.-P.: Motion capture using joint skeleton tracking and surface estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  15. 15.
    Gelfand, N., Mitra, N.J., Guibas, L.J., Pottmann, H.: Robust global registration. In: Symposium on Geometry Processing, p. 197 (2005)Google Scholar
  16. 16.
    Guan, P., Weiss, A., Bălan, A.O., Black, M.J.: Estimating human shape and pose from a single image. In: IEEE International Conference on Computer Vision (2009)Google Scholar
  17. 17.
    Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., Seidel, H.-P.: A statistical model of human pose and body shape. Comput. Graphic. Forum (Special Issue of Eurographics 2008), 2(28) (2009)Google Scholar
  18. 18.
    Huang, Q., Adams, B., Wicke, M., Guibas, L.J.: Non-rigid registration under isometric deformations. Comput. Graphic. Forum (Special Issue of SGP 2008), 27(5) (2008)Google Scholar
  19. 19.
    Jain V., Zhang H.: A spectral approach to shape-based retrieval of articulated 3d models. Comput. Aided Des. 39(5), 398–407 (2007)CrossRefGoogle Scholar
  20. 20.
    Johnson A.E., Hebert M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 433–449 (1999)CrossRefGoogle Scholar
  21. 21.
    Li, H., Sumner, R.W., Pauly, M.: Global correspondence optimization for non-rigid registration of depth scans. Comput. Graphic. Forum 27(5) (2008)Google Scholar
  22. 22.
    Liu D.C., Nocedal J.: On the limited memory method for large scale optimization. Math. Progr. B 45, 503–528 (1989)MathSciNetzbMATHCrossRefGoogle Scholar
  23. 23.
    Meunier, P., Shu, C., Xi, P.: Revealing the internal structure of human variability for design purposes. In: IEA World Congress on Ergonomics (2009)Google Scholar
  24. 24.
    Pauly, M., Mitra, N., Giesen, J., Gross, M., Guibas, L.: Example-based 3d scan completion. In: Symposium on Geometry Processing, p. 23 (2005)Google Scholar
  25. 25.
    Pearl J.: Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Mateo (1988)Google Scholar
  26. 26.
    Robinette, K., Daanen, H., Paquet E.: The CAESAR project: A 3-D surface anthropometry survey. In: Conference on 3D Digital Imaging and Modeling, pp. 180–186 (1999)Google Scholar
  27. 27.
    Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Conference on 3D Digital Imaging and Modeling (June 2001)Google Scholar
  28. 28.
    Styner, M., Rajamani, K.T., Nolte, L.-P., Zsemlye, G., Székely, G., Taylor, C.J., Davies, R.H.: Evaluation of 3d correspondence methods for model building. In: Information Processing in Medical Imaging, pp. 63–75 (2003)Google Scholar
  29. 29.
    van Kaick, O., Zhang, H., Hamarneh, G., Cohen-Or, D.: A survey on shape correspondence. In: Eurographics State-of-the-art Report (2010)Google Scholar
  30. 30.
    Wuhrer, S., Azouz, Z.B., Shu, C.: Semi-automatic prediction of landmarks on human models in varying poses. In: Canadian Conference on Computer and Robot Vision (2010)Google Scholar
  31. 31.
    Xi, P., Lee, W.-S., Shu, C.: Analysis of segmented human body scans. In: Graphics Interface (2007)Google Scholar
  32. 32.
    Yeo T., Sabuncu M., Vercauteren T., Ayache N., Fischl B., Golland P.: Spherical demons: fast diffeomorphic landmark-free surface registration. IEEE Trans. Med. Imaging 29(3), 650–668 (2010)CrossRefGoogle Scholar
  33. 33.
    Zhang, H., Sheffer, A., Cohen-Or, D., Zhou, Q., van Kaick, O., Tagliasacchi, A.: Deformation-driven shape correspondence. Comput. Graphic. Forum (Special Issue of SGP 2008) 27(5) (2008)Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.National Research Council of CanadaOttawaCanada

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