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An evaluation of garment fit to improve customer body fit of fashion design clothing

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Abstract

Currently, garment fit evaluation is one of the biggest bottlenecks for fashion design and manufacturing. In this paper, we proposed a garment fit prediction model using data learning technology based on Artificial Neural Networks. The inputs of the proposed model are digital clothing pressures measured by virtual try-on, while the output of the model is one of the three fit conditions—tight, fit, or loose. To acquire reliable learning data, virtual and real try-on experiments were carried out to collect input and output learning data, respectively. We collected 72 samples, each sample contains 20 clothing virtual pressure values and the corresponding fit values of the garment. After learning from the collected input and output experimental data, the proposed model can predict garment fit rapidly and automatically by inputting digital clothing pressures measured by virtual try-on. Test results showed that the prediction accuracy of fit evaluation model based on Back Propagation Artificial Neural Networks (BP-ANNs) is 93%. Compared with the 50% prediction accuracy of the traditional method, our proposed method has obvious advantages. This technology can be applied to the process of garment design and manufacturing to improve work efficiency.

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Funding

The work was financially supported by the National Natural Science Foundation of China, China (No. 61806161), the Natural Science Basic Research Program of Shaanxi Province, China (No. 2019JQ-848), the Innovation Ability Support Plan of Shaanxi Province-Young Science and Technology Star Project, China (No. 2020KJXX-083), the Teaching Reform Research Project of Undergraduate and Higher Continuing Education in Shaanxi province (No. 21BZ046), the Higher Education Science Research Project of Shaanxi Higher Education Society (No. XGH21143), China National Endowment for the Arts, China (No. 2018-A-05-(263)-0928), the Social Science Fund Project of Shaanxi Province, China (No. 2018K32), the Financial Support from the Fundamental Research Funds for the Central Universities (No. 2232021G-08), the Financial Support from International Cooperation Fund of Science and Technology Commission of Shanghai Municipality (No. 21130750100), and the Youth Innovation Team of Shaanxi Universities, China (No. 21JP048).

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Authors and Affiliations

  1. Apparel and Art Design College, Xi’an Polytechnic University, Xi’an 710048, China

    Kaixuan Liu, Hanhan Wu & Chun Zhu

  2. Fashion Institute, Shaanxi Fashion Engineering University, Xi’an 712046, China

    Hanhan Wu

  3. Shaanxi Key Laboratory of Intelligent Clothing Design, Xi’an Polytechnic University, Xian, 710048, China

    Kaixuan Liu

  4. GEMTEX Laboratory, ENSAIT, 59100, Roubaix, France

    Kaixuan Liu, Xianyi Zeng, Xuyuan Tao & Pascal Bruniaux

  5. Fashion and Art Design Institute, Donghua University, Shanghai, 200051, China

    Chun Zhu & Jianping Wang

  6. Key Laboratory of Clothing Design and Technology, Ministry of Education, Donghua University, Shanghai, 200051, China

    Chun Zhu & Jianping Wang

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  1. Kaixuan Liu
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Liu, K., Wu, H., Zhu, C. et al. An evaluation of garment fit to improve customer body fit of fashion design clothing. Int J Adv Manuf Technol 120, 2685–2699 (2022). https://doi.org/10.1007/s00170-022-08965-z

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