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Estimating 3D human shapes from measurements

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Abstract

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.

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  1. http://www.sae.org/standardsdev/tsb/cooperative/caesumm.htm.

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Acknowledgments

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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Authors and Affiliations

  1. Saarland University and Max-Planck Institut Informatik, Campus E 1.7, Saarbrücken, Germany

    Stefanie Wuhrer

  2. National Research Council of Canada, 1200 Montreal Road, Ottawa, Canada

    Chang Shu

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  1. Stefanie Wuhrer
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  2. Chang Shu
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Correspondence to Stefanie Wuhrer.

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Wuhrer, S., Shu, C. Estimating 3D human shapes from measurements. Machine Vision and Applications 24, 1133–1147 (2013). https://doi.org/10.1007/s00138-012-0472-y

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