A novel evaluation technique for human body perception of clothing fit

Liu, Kaixuan; Zhu, Chun; Tao, Xuyuan; Bruniaux, Pascal; Zeng, Xianyi; Wang, Jianping

doi:10.1007/s11042-023-14530-x

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…Additionally, it can export data from 3D clothing models and utilize that data with 3DCG characters (It's a type of three-dimensional character like animation, cartoon, etc.) (13)(14)(15) .…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Basic Shirt and Pant Fit Using Analytic Hierarchy Process-AHP and 3D CLO for 20-25 Years Bangladeshi Men

Nizam,

Rasel,

Ujevic

et al. 2024

IJST

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Background: The proportion of a garment to its body is known as garment fit. To ensure comfort between the human body and clothing, fit is very important. At present, 3D simulation of garment fit has great potential for business purposes. However, in previous analyses, there were expert opinions on manual fit analysis, which is very popular in the industry. Objectives: The main objectives of this research are to ensure manual clothing fits with a live model with 3D CLO software evaluation. Method: In this study, up to 50 textile experts from industries have chosen to analyze the analytical hierarchy process (AHP) to evaluate clothing fit. Findings: In this study, small, medium, and large-sized basic shirts and pants (20–25 years old) have been developed for live model fit evaluation. According to the analytical hierarchy process (AHP), among small, medium, and large-sized basic shirts and pants, one or two sizes of garments have been tested for garment fit evaluations. The results of these fit evaluations are satisfactory, according to 50 textile experts, and are the main output of the 3D CLO software. Novelty: The analytic Hierarchy Process (AHP) is fully adopted by the Bangladeshi newly developed size chart, which is enhanced by the 3D CLO software. Keywords: Analytic Hierarchy Process (AHP), 3D CLO, Shirts, Pant, Garments, Bangladesh

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Section: Methodsmentioning

confidence: 99%

Evaluation of Basic Shirt and Pant Fit Using Analytic Hierarchy Process-AHP and 3D CLO for 20-25 Years Bangladeshi Men

Nizam,

Rasel,

Ujevic

et al. 2024

IJST

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“…As a result, several ML-based computational intelligence approaches (i.e., naive Bayes classifier, back propagation artificial neural networks, decision tree C4.5, etc.) have been developed to support fashion designers in making decisions during the process of garment design, especially for fit evaluation [20,24,25] and clothing production [26,27]. These pieces of research present good cases and bright prospects for the application of ML techniques in the fashion industry.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Enhanced 3D Reverse Design Approach to Personalized Garments in Pursuit of Sustainability

et al. 2023

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The fashion industry is facing increasing pressure to move toward sustainable development, especially with concern to cost and environmental sustainability. Innovative digital technologies are regarded as a promising solution for fashion companies to resolve this issue. In this context, this paper put forth a new 3D reverse garment design approach embedded with a garment fit prediction and structure self-adaptive adjustment mechanism, using machine learning (ML) techniques. Initially, the 3D basic garment was drawn directly on the scanned mannequin of a specific consumer. Next, a probabilistic neural network (PNN) was employed to predict the garment’s fit. Afterwards, genetic algorithms (GA) and support vector regression (SVR) were utilized to estimate and control the garment structural parameters following the feedback of fit evaluation and the consumer’s personalized needs. Meanwhile, a comprehensive evaluation was constructed to characterize the quantitative relationships between the consumer profile and the designed garment profile (garment fit and styles). Ultimately, the desired garment which met the consumer’s needs was obtained by performing the routine of “design–fit evaluation–pattern adjustment–comprehensive evaluation”, iteratively. The experimental results show that the proposed approach provides a new solution to develop quality personalized fashion products (garments) more accurately, economically, and in an environmentally friendly way. It is feasible to facilitate the sustainable development of fashion companies by simultaneously reducing costs and negative impacts on the environment.

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