Wireframes have been proved useful as an intermediate layer of the neural network to learn the relationship between the human body and semantic parameters. However, the definition of the wireframe needs to have anthropological meaning and is highly dependent on experts’ experience. Hence, it is usually not easy to obtain a well-defined wireframe for a new set of shapes in available databases. An automated wireframe generation method would help relieve the need for the manual anthropometric definition to overcome such difficulty. One way to find such an automated wireframe generation method is to apply segmentation to divide the models into small mesh patches. Nevertheless, different segmentation approaches could have various segmented patches, thus resulting in diversified wireframes. How do these different sets of wireframes affect learning performance? In this paper, we attempt to answer this research question by defining several critical quantitative estimators to evaluate different wireframes’ learning performance. To find how such estimators influence wireframe-assisted learning accuracy, we conduct experiments by comparing different segmentation methods on human body shapes. We summarized several meaningful design guidelines for developing an automatic wireframe-aware segmentation method for human body learning with such verification.