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.
Recently, Baek and Lee @cite_19 presented a technique that uses hierarchical clustering to build a statistical model of the training data. The approach proceeds by clustering the training database and by performing a multi-cluster analysis of the training data. To predict a body shape based on a set of input measurements, the approach finds the shape within the learned shape space that best describes the measurements using an optimization of shape parameters. It is shown experimentally that accurate and visually pleasing body shapes are estimated when the input body sizes are inside the shape space spanned by the training data. This approach is conceptually similar to the first optimization step of our algorithm. Hence, we expect that this approach does not allow to model shape variations that are outside of the shape space spanned by the training data.
@cite_19
2077557812
The objective of this study is the development of a novel parametric human body shape modeling framework for integration into various product design applications. Our modeling framework is comprised of three phases of database construction, statistical analysis, and model generation. During the database construction phase, a 3D whole body scan data of 250 subjects are obtained, and their data structures are processed so as to be suitable for statistical analysis. Using those preprocessed scan data, the characteristics of the human body shape variation and their correlations with several items of body sizes are investigated in the statistical analysis phase. The correlations obtained from such analysis allow us to develop an interactive modeling interface, which takes the body sizes as inputs and returns a corresponding body shape model as an output. Using this interface, we develop a parametric human body shape modeling system and generate body shape models based on the input body sizes. In our experiment, our modeler produced reasonable results having not only a high level of accuracy but also fine visual fidelity. Compared to other parametric human modeling approaches, our method contributes to the related field by introducing a novel method for correlating body shape and body sizes and by establishing an improved parameter optimization technique for the model generation process.