Exemplar-based statistical model for semantic parametric design of human body

This paper presents an exemplar-based method to provide intuitive way for users to generate 3D human body shape from semantic parameters. In our approach, human models and their semantic parameters are correlated as a single linear system of equations. When users input a new set of semantic parameters, a new 3D human body will be synthesized from the exemplar human bodies in the database. This approach involves simpler computation compared to non-linear methods while maintaining quality outputs. A semantic parametric design in interactive speed can be implemented easily. Furthermore, a new method is developed to quickly predict whether the parameter values is reasonable or not, with the training models in the human body database. The reconstructed human bodies in this way will all have the same topology (i.e., mesh connectivity), which facilitates the freeform design automation of human-centric products.