Garment Fit Evaluation Using Machine Learning Technology

Liu, Kaixuan; Zeng, Xiaoyang; Bruniaux, Pascal; Tao, Xuyuan; Kamalha, Edwin; Wang, Jianping

doi:10.1007/978-981-13-0080-6_14

Cited by 12 publications

(8 citation statements)

References 45 publications

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“…Sun and Zhao (2017) focused solely on aspects of 3D printing to address challenges in garment manufacturing. Other studies (Chaw Hlaing et al , 2013; Lage and Ancutiene, 2017; Liu et al , 2018; Liu et al , 2017) investigated the application of AR in both prototype design in manufacturing sectors and apparel e-shopping. Garment manufacturers can now reap the benefits of 3D CAD systems in combination with BDA and AI to deploy intelligent design systems (Satam et al , 2011) which enhance the capability to respond to fast fashion.…”

Section: Resultsmentioning

confidence: 99%

“…While cluster 2 focuses on VTO for online apparel shopping, this section discusses VTO in garment design, which is a new field of study with recently published articles from 2017 onwards, combining CAD and online virtual technologies, and paving a path for I4.0 technologies. Only four articles (Dong et al , 2013; Liu et al , 2018; Liu et al , 2017; Tao et al , 2018) belong to this cluster, and these studies focus on how 3D-CAD and online virtual technologies can aid the rapid design of appropriately fitting clothes. Dong et al (2013) propose an expert system that compares and selects a range of products or designs using customer information.…”

Section: Resultsmentioning

confidence: 99%

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Technology adoption in the apparel industry: insight from literature review and research directions

Hoque

Rasiah

Furuoka

et al. 2021

RJTA

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Purpose This paper aims to identify key theoretical cornerstones and research trends in the apparel industry. It also compares theoretical bases with those of the general research domain in technology adoption literature and, thus, provides future policy guidelines for practitioners and research gaps for further studies. Design/methodology/approach Documents were collected from the Web of Science (core collection) database using systematic methods. The bibliometric coupling and co-citation analyses were conducted using VOSviewer software to construct theoretical cornerstones and research trends in the apparel industry. Findings Literature in the apparel industry focuses mainly on the diffusion of innovation and the theory of reasoned action. Hence, the literature lacks investigations of technology–organization–environment and institutional theories for technology adoption in the apparel industry. This study also traces six clusters of prevalent research trends: radiofrequency identification, virtual-try on technology for e-commerce, computer-aided design, Industry 4.0 technologies, virtual-try on technology in design and information technology. Originality/value Little research is done on theoretical cornerstones on technology adoption in the apparel industry. This study looks into the theoretical bases for technology adoption, research trends in the apparel supply chain and calls for future research necessities.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Technology adoption in the apparel industry: insight from literature review and research directions

Hoque

Rasiah

Furuoka

et al. 2021

RJTA

View full text Add to dashboard Cite

show abstract

“…A method based on 3D virtual-reality technology is used to simulate human activities and to measure clothing pressures by determining key elements of the wearing comfort is introduced in (Liu et al, 2018). A machine learning model based on Naïve Bayes classifier is used to predict garment fit as it shown in Figure 5.…”

Section: Artificial Intelligence For Design and Manufacturingmentioning

confidence: 99%

“…GAN are used widely in fashion and decoration (Liu et al, 2018;Meng et al, 2018;Zhao & Ma, 2018). An attribute GAN to generate clothing-match pairs is proposed by Liu et al, (2019).…”

Section: Artificial Intelligence For Design and Manufacturingmentioning

confidence: 99%

Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review

Jimeno-Morenilla

Azariadis

Molina-Carmona

et al. 2021

Computers in Industry

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The traditional manufacturing sectors (footwear, textiles and clothing, furniture and toys, among others) are based on small and medium enterprises with limited capacity on investing in modern production technologies. Although these sectors rely heavily on product customization and short manufacturing cycles, they are still not able to take full advantage of the fourth industrial revolution. Industry 4.0 surfaced to address the current challenges of shorter product life-cycles, highly customized products and stiff global competition. The new manufacturing paradigm supports the development of modular factory structures within a computerized Internet of Things environment. With Industry 4.0, rigid planning and production processes can be revolutionized. However, the computerization of manufacturing has a high degree of complexity and its implementation tends to be expensive, which goes against the reality of SMEs that power the traditional sectors. This paper reviews the main scientific-technological advances that have been developed in recent years in traditional sectors with the aim of facilitating the transition to the new industry standard.

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“…Liu et al [9] proposed a machine learning-based model to evaluate garment fit. e inputs of the proposed model are digital clothing pressures, and the output is the predicted result of garment fit.…”

Section: Fit Evaluationmentioning

confidence: 99%

Dual Adaptive Adjustment for Customized Garment Pattern

Zhu

Peng

Asl

2019

Scientific Programming

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We present a dual adaptive garment slice adjustment technique for automatic resizing of apparel products with variant body shapes, and this technique can quickly generate clothed characters. Our first contribution is to propose a novel fit evaluation method. When a 2D garment pattern and a 3D draped garment have the same triangle topology connection, we calculate the shape variable of each triangle and output heat map simultaneously. For sewing a pattern to a newly targeted human body, we propose a fully automatic adjustment method that conforms to the body structure and is composed of two stages. In the coarse auto adjustment (CAA) stage, we propose a method of controlling the size of a garment by the length of a bounding box in five parts of the human body. Then, the garment pattern is automatically adjusted using the measured dimension, by stretching or shrinking. In the fine auto adjustment (FAA) stage, boundary vertices control the shape in the adjustment process. For better matching with the body, the vertices of the garment pattern boundary are relocated with the calculated moving distance and moving direction. As demonstrated in the results, our method enables fully automatic adjustment, preserving the original pattern style of garments between characters with vastly body shapes. Compared with the state-of-art 2D editing method, our proposed approach leads to time saving, and it achieves realistic garment effect compared to auto fitting methods.

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Garment Fit Evaluation Using Machine Learning Technology

Cited by 12 publications

References 45 publications

Technology adoption in the apparel industry: insight from literature review and research directions

Technology adoption in the apparel industry: insight from literature review and research directions

Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review

Dual Adaptive Adjustment for Customized Garment Pattern

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