Machine Vision and Applications

, Volume 24, Issue 6, pp 1133–1147 | Cite as

Estimating 3D human shapes from measurements

  • Stefanie Wuhrer
  • Chang Shu
Original Paper


Recent advances in 3D imaging technologies give rise to databases of human shapes, from which statistical shape models can be built. These statistical models represent prior knowledge of the human shape and enable us to solve shape reconstruction problems from partial information. Generating human shape from traditional anthropometric measurements is such a problem, since these 1D measurements encode 3D shape information. Combined with a statistical shape model, these easy-to-obtain measurements can be leveraged to create 3D human shapes. However, existing methods limit the creation of the shapes to the space spanned by the database and thus require a large amount of training data. In this paper, we introduce a technique that extrapolates the statistically inferred shape to fit the measurement data using non-linear optimization. This method ensures that the generated shape is both human-like and satisfies the measurement conditions. We demonstrate the effectiveness of the method and compare it to existing approaches through extensive experiments, using both synthetic data and real human measurements.


Human models Statistical prior  Three-dimensional reconstruction 



We thank the volunteers for participating in the experiment. Furthermore, we thank Pengcheng Xi for helpful discussions and for providing us with the training data, Neil Lawrence for providing us with the SGPLVM code, and the anonymous reviewers for helpful comments. This work has partially been funded by the Cluster of Excellence Multimodal Computing and Interaction within the Excellence Initiative of the German Federal Government.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Saarland University and Max-Planck Institut InformatikSaarbrückenGermany
  2. 2.National Research Council of CanadaOttawaCanada

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