Full body statistical shape modeling with posture normalization

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.