Clothes Size Prediction from Dressed-Human Silhouettes

  • Dan Song
  • Ruofeng TongEmail author
  • Jian Chang
  • Tongtong Wang
  • Jiang Du
  • Min Tang
  • Jian J. Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10582)


We propose an effective and efficient way to automatically predict clothes size for users to buy clothes online. We take human height and dressed-human silhouettes in front and side views as input, and estimate 3D body sizes with a data-driven method. We adopt 20 body sizes which are closely related to clothes size, and use such 3D body sizes to get clothes size by searching corresponding size chart. Previous image-based methods need to calibrate camera to estimate 3D information from 2D images, because the same person has different appearances of silhouettes (e.g. size and shape) when the camera configuration (intrinsic and extrinsic parameters) is different. Our method avoids camera calibration, which is much more convenient. We set up our virtual camera and train the relationship between human height and silhouette size under this camera configuration. After estimating silhouette size, we regress the positions of 2D body landmarks. We define 2D body sizes as the distances between corresponding 2D body landmarks. Finally, we learn the relationship between 2D body sizes and 3D body sizes. The training samples for each regression process come from a database of 3D naked and dressed bodies created by previous work. We evaluate the whole procedure and each process of our framework. We also compare the performance with several regression models. The total time-consumption for clothes size prediction is less than 0.1 s and the average estimation error of body sizes is 0.824 cm, which can satisfy the tolerance for customers to shop clothes online.


Clothes size prediction Body size estimation Regression methods 



The research is supported in part by NSFC (61572424) and the People Programme (Marie Curie Actions) of the European Unions Seventh Framework Programme FP7 (2007-2013) under REA grant agreement No. 612627-“AniNex”. Min Tang is supported in part by NSFC (61572423) and Zhejiang Provincial NSFC (LZ16F020003).


  1. 1.
    Song, D., Tong, R., Chang, J., Yang, X., Tang, M., Zhang, J.J.: 3D body shapes estimation from dressed human silhouettes. Comput. Graph. Forum 35(7), 147–156 (2016)CrossRefGoogle Scholar
  2. 2.
    Zhu, S., Mok, P.Y.: Predicting realistic and precise human body models under clothing based on orthogonal-view photos. Procedia Manufact. 3, 3812–3819 (2015)CrossRefGoogle Scholar
  3. 3.
    Lin, Y.L., Wang, M.J.J.: Automatic feature extraction from front and side images. In: IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008, pp. 1949–1953. IEEE (2008)Google Scholar
  4. 4.
    Nguyen, H.T.: Automatic anthropometric system development using machine learning. BRAIN Broad Res. Artif. Intell. Neurosci. 7(3), 5–15 (2016)Google Scholar
  5. 5.
    Cheng, K.L., Tong, R.F., Tang, M., Qian, J.Y., Sarkis, M.: Parametric human body reconstruction based on sparse key points. IEEE Trans. Visual Comput. Graph. 22(11), 2467–2479 (2016)CrossRefGoogle Scholar
  6. 6.
    Dollar, P., Welinder, P., Perona, P.: Cascaded pose regression. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1078–1085 (2010)Google Scholar
  7. 7.
    Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. Int. J. Comput. Vis. 107(2, SI), 177–190 (2014)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Cao, C., Weng, Y., Lin, S., Zhou, K.: 3D shape regression for real-time facial animation. ACM Trans. Graph. 32(4), 41:1–41:10 (2013)CrossRefzbMATHGoogle Scholar
  9. 9.
    Shao, H., Chen, S., Zhao, J., Cui, W., Yu, T.: Face recognition based on subset selection via metric learning on manifold. Front. Inf. Technol. Electron. Eng. 16(12), 1046–1058 (2015)Google Scholar
  10. 10.
    Hoerl, A.E., Kennard, R.W.: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1), 55–67 (1970)CrossRefzbMATHGoogle Scholar
  11. 11.
    Liaw, A., Wiener, M.: Classification and regression by randomForest. R news 2(3), 18–22 (2002)Google Scholar
  12. 12.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  14. 14.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152 (1992)Google Scholar
  15. 15.
    Chen, Y., Cipolla, R.: Learning shape priors for single view reconstruction. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 1425–1432. IEEE (2009)Google Scholar
  16. 16.
    Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., Davis, J.: SCAPE: shape completion and animation of people. ACM Trans. Graph. 24(3), 408–416 (2005)CrossRefGoogle Scholar
  17. 17.
    Bălan, A.O., Black, M.J.: The naked truth: estimating body shape under clothing. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 15–29. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88688-4_2 CrossRefGoogle Scholar
  18. 18.
    Boisvert, J., Shu, C., Wuhrer, S., Xi, P.: Three-dimensional human shape inference from silhouettes: reconstruction and validation. Mach. Vis. Appl. 24(1), 145–157 (2013)CrossRefGoogle Scholar
  19. 19.
    Hasler, N., Stoll, C., Sunkel, M., Rosenhahn, B., Seidel, H.P.: A statistical model of human pose and body shape. Comput. Graph. Forum 28(2), 337–346 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dan Song
    • 1
  • Ruofeng Tong
    • 1
    Email author
  • Jian Chang
    • 2
  • Tongtong Wang
    • 1
  • Jiang Du
    • 1
  • Min Tang
    • 1
  • Jian J. Zhang
    • 2
  1. 1.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina
  2. 2.NCCA, Bournemouth UniversityPooleUK

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