Deep 3D Body Landmarks Estimation for Smart Garments Design
Annalisa Baronetto, et. al
Chair of Digital Health | Friedrich-Alexander-Universität Erlangen-Nürnberg
Abstract: We propose a framework to automatically extract body landmarks and related measurements from 3D body scans and replace manual body shape estimation in fitting smart garments. Our framework comprises five steps: 3D scan acquisition and segmentation, 2D image conversion, extraction of body landmarks using a Convolutional Neural Network (CNN), back projection and mapping of extracted landmarks to 3D space, body measurements estimation and tailored garment generation. We trained and tested the algorithm on 3000 synthetic 3D body models and estimated body landmarks required for T-Shirt design. The results show that the algorithm can successfully extract 3D body landmarks of the upper front with a mean error of 1.01 cm and of the upper back with a mean error of 0.78 cm. We validated the framework the framework in automated tailoring of an electrocardiogram (ECG)-monitoring shirt based on the predicted landmarks. The ECG shirt can fit all evaluated body shapes with an average electrode-skin distance of 0.61 cm.
Narrowband Application in Intelligent Fire Protection System
