Automatic body measurement by neural networks

Size prediction and garment customization are two main goals of body measurement for garment design. Traditional body measurement, involving manual measurement and trying clothes in person, is time-consuming and not cost-efficient. With the help of 3D body scanner and neural networks, body measureme...

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Bibliographic Details
Main Author: Zhao, Jingyi
Other Authors: Qian Kemao
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/77220
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Institution: Nanyang Technological University
Language: English
Description
Summary:Size prediction and garment customization are two main goals of body measurement for garment design. Traditional body measurement, involving manual measurement and trying clothes in person, is time-consuming and not cost-efficient. With the help of 3D body scanner and neural networks, body measurement can be fast and precise, thus reducing the cost. This project introduces neural network models to predict body sizes and the measurements used to customize clothes from various body data. In this project, three kinds of input data are used: raw 3D point clouds of human bodies, key body locations, and estimated body measurements. Raw point clouds are collected by scanning the participants’ body, and key body locations and estimated measurements are automatically computed by existing software. Then the manual measurement is applied to the participants to obtain the size labels and useful measurements for garment customization, which are used as the ground-truth values of output data. Different network structures are utilized for different kinds of input data. The results show that neural networks can achieve decent performance in predicting measurements for making clothes, and different input data can lead to different accuracies of prediction. The models can be further improved with a larger amount of data, in order to make it production-ready.