Building 3-D Human Data Based on Handed Measurement and CNN

Abstract

3-dimension (3-D) printing technology is growing strongly with many applications, one of which is the garment industry. The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics. This paper proposes a method to construct 3-D human models by applying deep learning. We calculate the location of the main slices of the human body, including the neck, chest, belly, buttocks, and the rings of the extremities, using pre-existing information. Then, on the positioning frame, we find the key points (fixed and unaltered) of these key slices and update these points to match the current parameters. To add points to a star slice, we use a deep learning model to mimic the form of the human body at that slice position. We use interpolation to produce sub-slices of different body sections based on the main slices to create complete body parts morphologically. We combine all slices to construct a full 3-D representation of the human body.