Fit evaluation of virtual garment try-on by learning from digital pressure data

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dc.contributor.authorLiu, Kaixuan
dc.contributor.authorZeng, Xianyi
dc.contributor.authorBruniaux, Pascal
dc.contributor.authorWang, Jianping
dc.contributor.authorKamalha, Edwin
dc.contributor.authorTao, Xuyuan
dc.date.accessioned2022-12-04T13:00:37Z
dc.date.available2022-12-04T13:00:37Z
dc.date.issued2017
dc.description.abstractPresently, 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.en_US
dc.identifier.citationKaixuan 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.2017.07.007en_US
dc.identifier.other10.1016/j.knosys.2017.07.007
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/5821
dc.language.isoenen_US
dc.publisherKnowledge-Based Systemsen_US
dc.subjectDigital clothing pressureen_US
dc.subjectSupport vector machinesen_US
dc.subjectNaive Bayesen_US
dc.subjectActive learningen_US
dc.subjectEase allowanceen_US
dc.subjectReal try-onen_US
dc.titleFit evaluation of virtual garment try-on by learning from digital pressure dataen_US
dc.typeArticleen_US
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