Fit evaluation of virtual garment try-on by learning from digital pressure data
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Knowledge-Based Systems
Abstract
Presently, garment fit evaluation mainly focuses on real try-on, and rarely deals with
virtual try-on. With the rapid development of E-commerce, there is a profound growth
of garment purchases through the internet. In this context, fit evaluation of virtual
garment try-on is vital in the clothing industry. In this paper, we propose a Naive
Bayes-based model to evaluate garment fit. The inputs of the proposed model are
digital clothing pressures of different body parts, generated from a 3D garment CAD
software; while the output is the predicted result of garment fit (fit or unfit). To
construct and train the proposed model, data on digital clothing pressures and garment
real fit was collected for input and output learning data respectively. By learning from
these data, our proposed model can predict garment fit rapidly and automatically
without any real try-on; therefore, it can be applied to remote garment fit evaluation in
the context of e-shopping. Finally, the effectiveness of our proposed method was
validated using a set of test samples. Test results showed that digital clothing pressure
is a better index than ease allowance to evaluate garment fit, and machine
learning-based garment fit evaluation methods have higher prediction accuracies.
Description
Keywords
Digital clothing pressure, Support vector machines, Naive Bayes, Active learning, Ease allowance, Real try-on
Citation
Kaixuan Liu , Xianyi Zeng , Pascal Bruniaux , Jianping Wang , Edwin Kamalha , Xuyuan Tao , Fit evaluation of virtual garment try-on by learning from digital pressure data, Knowledge-Based Systems (2017), doi: 10.1016/j.knosys.2017.07.007