Abstract
Realistic virtual mannequins, that represent body shapes that occur in the target population, are valuable tools for product developers who design near-body products. Statistical shape modeling is a promising approach to map out the variability of body shapes. The strength of statistical shape models (SSM) is their ability to capture most of the shape variation with only a few shape modes. Unfortunately, the shape variation captured by SSMs of human bodies is often polluted by variations in posture, which substantially reduces the compactness of those models. In this paper, we propose a fast and data driven framework to build a posture invariant SSM. The normalized SSM is shown to be substantially more compact than the non-normalized SSM. Using five shape modes, the normalized SSM is 23% more compact than the non-normalized SSM.
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Acknowledgements
This work was supported by the Agency for Innovation by Science and Technology in Flanders (IWT-SB 141520).
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Danckaers, F., Huysmans, T., Hallemans, A., De Bruyne, G., Truijen, S., Sijbers, J. (2018). Full Body Statistical Shape Modeling with Posture Normalization. In: Cassenti, D. (eds) Advances in Human Factors in Simulation and Modeling. AHFE 2017. Advances in Intelligent Systems and Computing, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-319-60591-3_39
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DOI: https://doi.org/10.1007/978-3-319-60591-3_39
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