Garment Fit Research Articles

Digital Garment Alteration

AbstractGarment alteration is a practical technique to adapt an existing garment to fit a target body shape. Typically executed by skilled tailors, this process involves a series of strategic fabric operations—removing or adding material—to achieve the desired fit on a target body. We propose an innovative approach to automate this process by computing a set of practically feasible modifications that adapt an existing garment to fit a different body shape. We first assess the garment's fit on a reference body; then, we replicate this fit on the target by deriving a set of pattern modifications via a linear program. We compute these alterations by employing an iterative process that alternates between global geometric optimization and physical simulation. Our method utilizes geometry‐based simulation of woven fabric's anisotropic behavior, accounts for tailoring details like seam matching, and incorporates elements such as darts or gussets. We validate our technique by producing digital and physical garments, demonstrating practical and achievable alterations.

Comparison of actual and virtual pressure of athletic clothing in active poses

PurposeThe circular design process in contemporary fashion design, from two-dimensional (2D) sketching and pattern making to three-dimensional (3D) prototypes, can be facilitated by virtual prototyping. Virtual pressure representations on avatars provide visual and quantitative information regarding garment fit and comfort, which are particularly important for active wear. The purpose of this study is to investigate the benefits of using avatars in active poses from 3D body scans and the use of digital 3D tools for the design process and the prediction of fit of active wear.Design/methodology/approachThis research initially explores virtual fit of cycling wear in active poses and compares the actual pressure values from humans with virtual pressure maps on custom avatars made from body scans in cycling poses across a range of sizes.FindingsSimilar fit results were achieved visually in both the standing and cycling poses. However, the comparisons showed no correlation between the actual and virtual pressure data. Of the 32 cases representing different combinations of the parameters of this research (four sizes, two garment types, four active poses), the differences were significant. The results suggest that, rather than providing a direct correlation with pressure values on the body, the main value of avatar data is in providing comparative visual support for fit evaluation.Originality/valueThe approach taken in this research, which considers the active pose and the size range, potentially contributes to the improvement of virtual fit technology, and its more effective use in apparel product development and fit evaluation.

Fit models’ roles in identifying fit issues in the apparel technical design process and implications for improving 3D virtual fitting

Fit sessions are essential in making well-fitted clothing. During these sessions, apparel fit is determined iteratively by a team of designers, technical designers, fit models, and merchandisers. With the advent of the digitalization of new product development processes in the fashion industry, fit sessions have been seen as a bottleneck for they are still held in person. However, the process is currently still irreplaceable, and what makes the feedback provided by fit models important is an area that has never been tapped. Therefore, the present study aimed to understand fit models’ role in fit sessions as well as how they assess the fit of garments and deliver their feedback. On-site observation during fit sessions and individual interviews with fit models were conducted. It was found that fit models gave comfort, fit, and tactile comments by testing garments while standing and moving between several postures. They had knowledge that overlapped with those of technical designers and designers and this empowerment enabled them to take part in the decision-making process in fit sessions. It also was found that fit models’ feedback on garments was essential as they were the first people to try on the garments and present the customers’ points of view.

Individualized generation of women's prototype based on the classification of body shape

The traditional prototype method only relies on the basic dimensions of the human body such as bust girth and back length to make the prototype pattern, ignoring the differences of human body details dimensions. In order to improve the fit of garments, the research on intelligent generation technology of individualized garment patterns has become one of the hot spots in the field of clothing. Based on the 3D body scanning technology, we proposed a method for generating prototype pattern based on body shape classification. The proposed method takes as inputs the body parameters (width, thickness and angle), while the output of the method is one of the four pattern generation rules-"Y" shape, "V" shape, "I" shape, or "X" shape. 207 subjects were divided into four categories based on the body angle parameters, namely "Y" shape, "V" shape, "I" shape and "X" shape, and a shape recognition model was built. The pattern database is required to obtain pattern generation rules by 3D point cloud reconstruction and flattening. Combined with the human body shape parameters, the mathematical formulas of the feature points on the pattern landmarks are built. The accuracy of body shape recognition model is 97.4%. The error analysis of the predicted pattern parameters shows that 90% of the pattern feature points have a goodness of fit above 0.7. In the 5 main landmarks, the proportion of pattern within the absolute error range is more than 80%, indicating that the prediction effect of the pattern is good. This method can be applied to the process of automatic pattern generation system based 2D measurement to improve work efficiency.

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

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

Analysis of garment fit problems and body measurements of Ethiopian young female consumers

Purpose This paper aims to investigate fit problems with some ready-to-wear garments, analyse the body measurements of Ethiopian young female consumers and draw implications for the improvement of ready-to-wear garment fit. Design/methodology/approach A random sample of 970 university students aged 18–35 years were interviewed, and their 35 body dimensions were measured manually according to the procedures in ISO 8559: 2017. The fit problems and body measurements were examined with the body mass index (BMI) and ethnicity of the subjects. Moreover, 15 body dimensions were compared with that of Chinese and US females of similar age. Findings The results showed that fit problems are frequent in lower garments for underweight consumers. Nearly consistent and smaller differences in body measurements were observed with BMIs and ethnicity of the subjects, while inconsistent and larger differences were found among the subjects from the three countries. The Ethiopian subjects were smaller than the Chinese in height and weight, between the Chinese and US females in most body measurements, and larger than the US subjects in across shoulder and arm and shoulder lengths. The results alarm the need for the development of Ethiopian national garment size standards. Originality/value The paper relates ready-to-wear garment fit issues to demographic factors and demographic factors to body measurements. Moreover, it considers young female consumers in Ethiopia, an African country with less explored consumer needs for clothing.

Investigation of readymade garment fit issues and analysis of anthropometric measurements of Ethiopian young men

This paper presents analyses of anthropometric measurements of Ethiopian young men aged 18–30 years and draws implications for the improvement of the fit of readymade clothing. Thirty-five anthropometric dimensions of 4022 subjects were measured according to ISO 8559:2017. The anthropometric dimensions were described with mean, standard deviation, and some percentiles. Differences in the anthropometric measurements with the body mass index (BMI) and ethnicity of the subjects were examined by analysis of variances. Furthermore, differences in some anthropometric measurements from that of Indian and Greek young men of similar ages were tested by one-sample t-tests. The result showed that the average BMI of the subjects was 20.63 which is normal. Statistically significant mean differences were observed among the subjects with different BMIs and ethnicities in most of the anthropometric dimensions. The anthropometric measurements of the subjects were mostly larger than the Indians and smaller than the Greek men.

Tailoring Garment Fit for Personalized Body Image Enhancement: Insights from Digital Fitting Research

In the context of the Fashion Apparel Industry 4.0, a transformative evolution is directed towards the Online Apparel Mass Customization (OAMC) strategy, which provides efficient and personalized apparel product solutions to consumers. A critical challenge within this customization process is the determination of sizes. While existing research addresses comfort evaluation in relation to wearer and garment fit, little attention has been given to how garment fit influences the wearer’s body image, which is also an important purchase consideration. This study investigates the impact of garment fit on the wearer’s body scale perception using quantitative research design. A digital dataset of avatars, clothed in varying sizes of T-shirts, were created for the body scale perception experiment, and an Artificial Neural Network (ANN) model was developed to predict the effect of T-shirt fit on body image. With only a small number of garments and body measurements as inputs, the ANN model can accurately predict the body scales of the clothed persons. It was found that the effect of apparel fit on body image varies depending on the wearer’s gender, body size, and shape. This model can be applied to enhance the online garment shopping experience with respect to personalized body image enhancement.

AnMePa: development of a new method for preparing trousers pattern by using the antropometric measurement system

PurposeThis study establishes the principles and process steps of a new basic trousers pattern using measurements obtained according to the rules of the anthropometric measurement system. The newly developed pattern-making system in this study will be called the “Anthropometric Measurements Based Pattern Making System” (AnMePa). It is aimed at producing trousers that are more fitting to the body, thanks to this pattern-making system.Design/methodology/approachIn this research, four pattern-making systems used in many parts of the world were compared with the “Anthropometric Measurements Based Pattern Making System” (AnMePa) with regard to the overall appearance and body fit of trousers prepared according to these systems. 10 virtual mannequins (VM) with different adult female body measurements were created, and trousers patterns were prepared for these mannequins. The trousers’ patterns were made and dressed on the mannequins in a 3D virtual dressing system. The body fit of the virtual garments was evaluated by five experts. The scores given by the experts were evaluated using the fuzzy logic method.FindingsAccording to the results, it is seen that the new basic trousers pattern developed by utilizing the anthropometric measurement system, AnMePa, provides the best body fit among the basic trousers patterns created according to the other examined pattern-making systems. The combination of 3D virtual dressing and fuzzy logic in the evaluation of garment body fit is considered an innovative method for the future of fashion design and production.Originality/valueIn the developed AnMePa, unlike the existing pattern-making systems, values that can be associated with the body measurements of individuals in a way that could be suitable for each community were used instead of constant values in the pattern-making process. Furthermore, the integration of 3D virtual fitting and fuzzy logic in assessing garment fit is considered a pioneering approach with significant implications for the future landscape of fashion design and production.

Predicting consumers’ garment fit satisfactions by using machine learning

Abstract The objectives of this study were to apply alternative machine learning (ML) algorithms to predict consumers’ garment fit satisfactions (real fit satisfaction [RFS]) and compare the efficiencies of these algorithms to predict RFS. Skirts made from different fabrics were used as test garments. Mechanical properties of the skirts’ fabrics were assigned as predictor variables to estimate RFS. Study participants’ virtual body models were created by using 3D body scanner and used for virtual fitting. Each participant physically tried on the skirts and evaluated the fit. Participants also viewed the skirt simulations on their avatars and evaluated the virtual fit, which represented participants’ virtual fit satisfactions (VFS). Random Forest (RF), support vector machine (SVM), and conditional tree (CT) algorithms were used to learn from the data to predict participants’ RFSs. The mean correlations between the predicted and observed RFS values in the validation sets were 0.74 (RF), 0.70 (SVM-linear kernel), 0.72 (SVM-radial kernel), and 0.55 (CT). According to the variable importance analysis, VFS had the highest importance among 35 predictor variables. ML is used mostly for sales forecasting and manufacturing purposes in the fashion industry. However, garment fit, which restrains consumers from shopping online, did not get enough attention in ML studies. Besides, the ML algorithms used in fashion and apparel studies are often genetic algorithms and neural networks; therefore, there is a need to test other algorithm types. In this study, we offered alternative ML algorithms (i.e., RF, SVM, and CT) to predict consumers’ garment fit satisfactions.

VR Designer: enhancing fashion showcases through immersive virtual garment fitting

This paper introduces a Virtual Reality (VR) application tailored for fashion designers and retailers, transcending traditional garment design and demonstration boundaries by presenting an immersive digital garment showcase within a captivating VR environment. Simulating a virtual retail store, designers navigate freely, selecting from an array of avatar-garment combinations and exploring garments from diverse perspectives. This immersive experience offers designers a precise representation of the final product’s aesthetics, fit, and functionality on the human body. Our application can be considered as a pre-manufacturing layer, that empowers designers and retailers with a precise understanding of how the actual garment will look and behave. Evaluation involved comprehensive feedback from both professional and undergraduate fashion designers, gathered through usability testing sessions.

Exploring Apparel Design Students’ Willingness to Use Virtual Reality in the Sketching Phase of the Fashion Design Process

Virtual reality (VR) is a computer-generated experience through projection of shapes, objects, and scenic views via headset or helmet device and interpreted as a genuine realistic environment. The interpretation of the highly realistic projection from the eye creates an immersive experience for the user. The use of VR technology is utilized in an array of industry applications as well as learning/training spaces. In particular, when VR is used in an educational setting, studies have shown positive or improved student achievement for a variety of academic disciplines in conjunction with creativity, inspiration, engagement, and motivation. VR used in industry applications, specifically within apparel industries, has been reported to enhance efficiencies in retail, enable virtual garment fitting, and reduction of physical samples in the apparel design and product development process. Regardless of these demonstrated favorable outcomes from education and industry, few research studies have investigated students’ willingness to adopt VR as a tool for use within their desired academic discipline (feeding into their future career path). As use in the apparel design and product development process has been reported, an opportunity exists to explore VR as an educational tool to ideate fashion design ideas in an individual’s apparel design process. Thus, the goal of this study was to provide initial insights into students’ willingness to use VR as a sketching tool, compared to traditional sketching tools, during the apparel design process. A multiple case study design was employed to examine the phenomenon in eight selected undergraduate Apparel Design students. The study was guided through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to examine students’ willingness based on performance expectancy, effort expectancy, social influence, and facilitating conditions to use VR. A learning module was developed in a 300-level apparel design course specifically to explore VR as a sketching tool in their design ideation process. Students received a demonstration for how to use the Oculus Quest VR equipment and experimentation/practice time using the equipment in conjunction with the Gravity Sketch (GS) VR application which allowed student users to draw in virtual space on an imported preexisting dress form. Following the practice time, students then completed an apparel design activity using the VR tool. Students then completed a survey in which data was collected through mixed methods by questionary scales and open-ended questions. The findings suggest a relationship between social willingness to adopt VR in their design ideation process while also providing a structure for future researchers to expand the use of this technology in apparel design coursework.

Cluster Size Intelligence Prediction System for Young Women’s Clothing Using 3D Body Scan Data

This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer’s body, thus enhancing both the fit and aesthetic value of the clothing.

The influence of garment quality evaluation on fashion innovation of dressmakers in Ghana

Purpose The innovativeness of dressmakers is a concern to respondents to satisfy their clothing needs. The purpose of this study is to determine the criteria that respondents use to judge the quality of clothing and its influences on the innovative ability of dressmakers in the clothing manufacturing industries. Design/methodology/approach Three hundred and ninety-seven (397) respondents in the Takoradi Metropolis of Ghana filled out a questionnaire, and the results were used to compile data for the study. The sample size was calculated using Miller and Brewer formula. The data was analysed using structural equational modelling with the SmartPLS v.4 software. Findings The results showed that respondents are very interested in the calibre of clothing produced by their dressmakers. However, the study revealed that when evaluating the quality of a garment, respondents do not simply accept what has been sewn for them but also consider the performance, appearance, fit and shape of the garment. Findings revealed that there was a significant relationship between garment appearance quality (t = 2.605; p < 0.05), garment performance quality (t = 3.915; p < 0.05), garment shape quality (t = 6.248; p <0.05) and fashion innovations. Subsequently, the evaluation of garment fit quality by respondents revealed it does not bring about innovations (t = 1.310; p > 0.05). Practical implications The continuous evaluation of custom-made clothing from customers will go a long way towards highlighting the relative criteria they use to evaluate the innovation of dressmakers. This will help improve the creativity of the dressmakers since such feedback will help them understand and innovate their production skills to meet the preferences of customers. Originality/value The present study provides an in-depth understanding of how garment quality evaluation by customers influences the innovation of dressmakers in Takoradi, Ghana. The constructs were developed for the study to capture the appropriate data from customers for the study. This presents an evaluation criterion on four garment quality variables imperative for use or modification by other studies.

A Conceptual Review of Uniform Wear Attributes Identification for Size Fit

With the accessibility of fast fashion, consumers have opportunities to purchase fashion clothes that are current trends, besides providing them options to choose the right garment at any time and place. However, dissatisfaction with garment fit has always been a challenge. Size fit provides a good fit, emphasizing comfort, ease of wearing, and shape. This research has conceptualized several attributes that could enhance the excellent fit of uniform wear that are divided into functional, aesthetic, and emotional. Employing dimensional attributes would enhance the performance of workwear besides pleasing the wearers due to comfort and making them look attractive.

Innovative Application of Modularized Virtual Interaction Design Technology in Clothing Design

Abstract CAD-based clothing design is known for its ease and flexibility, yet it often lacks intuitiveness and requires extensive experience for successful garment creation. To address these limitations, this paper presents a novel modular virtual interactive clothing design system using OpenGL and 3D modeling technologies. This system employs virtual reality to enable three-dimensional interactions, creating 3D garments that are then mapped onto a 2D plane for further evaluation. The evaluation module assesses the garment’s balance, line coherence, looseness, and wrinkle aesthetics to finalize the design. The research demonstrates that design discrepancies are kept below 1% in this virtual setting. The system’s modular nature also ensures satisfactory garment fit and appearance, underscoring its practical significance and potential impact.

All-3D apparel development: Establishing the rules to enable 3D weaving from 3D digital garments

Three-dimensional (3D) digital models can enable designers to tailor garments to specific measurements and preferences, ensuring a perfect fit and personalized aesthetics. This precision would allow to achieve seamless integration between the digital representation, the customer’s body shape, and the physical woven garment outcome. If the weaving machine can be automated, fast, cost-effective, custom, exact fit garments could be produced in a matter of days due to the elimination of the 2D patternmaking steps in the garment process. Weaving a 3D shape, which was designed in a 3D software program, using a circular weaving machine requires certain grid rules applied to the 3D shape to optimize the weaving process, minimize material wastage, and ensure a successful outcome. In the present study, we proposed rules for weaving 3D surfaces from body scan files to enable future algorithm development. These rules aim to address factors such as weaving efficiency, structural integrity, and the limitations of circular weaving machines.

한국인 20대 여성 표준체형을 바탕으로 한 상의 원형 패턴 비교 연구

Background/Objectives: The relationship between somatotype and patterns in garment fitting is important when it comes to clothing fitness. In this study, we analyze the relationship and garment sample fitness between standard somatotypes of Korean females in their twenties and seven block patterns mainly used in fashion-related educational institutions. Also, we propose a block pattern correction method for Korean females in their twenties. Three types of bodice blocks, such as new bunka, Armstrong, and Natalie bray, were used in this study, along with four types of torsos blocks, such as L&N, Müiler, Secolli, and Esmod.
 Methods: The block pattern correction was conducted by analyzing the strengths and weaknesses of each block for Korean females in their twenties through sensory evaluations by seven professionals on garment samples with original blocks. Through after- correction sensory evaluations, we determined the degrees of improvements made on the corrected-block patterns' fitness, and compared the original blocks to the corrected-block patterns and their garment samples.
 Results: Comparing the sensory evaluation results of the original and the after-correction shows that garment samples of the after-correction are more suitable for body types of Korean females in their twenties. We expect that the proposed block correction method, which analyzes the characteristics of each block, is useful when using the block patterns.
 Conclusion/Implications: From this study, we hope to see future studies on the development of regression equations and pattern-making formulas that are easily applied to various body types and sizes, as well as a correction method that considers the existence of sleeves and body movements.

Muslim Dress Code and Its Health Benefits

This article provided an overview of the history and benefits of Muslim dress code. It traced the origins and evolution of modest clothing styles prescribed in Islam and explored regional variations across Muslim majority cultures. Key benefits of adhering to Islamic dress standards around modesty are examined, including spiritual discipline, health aspects of loose fitting garments, and cultural identity affirmation. Common types of traditional Muslim attire such as hijāb, jalbāb and abāya are defined. The paper also looked at modern trends integrating Islamic dress principles with contemporary fashion design. Issues pertaining to social discussions on Muslim dress code in the context of religious freedom, cultural integration and stereotyping are critically analyzed.

Revolutionizing the Garment Industry 5.0: Embracing Closed-Loop Design, E-Libraries, and Digital Twins

This study presents an innovative approach for modernizing the garment industry through the fusion of digital human modeling (DHM), virtual modeling for fit sizing, ergonomic body-size data, and e-library resources. The integration of these elements empowers manufacturers to revolutionize their clothing design and production methods, leading to the delivery of unparalleled fit, comfort, and personalization for a wide range of body shapes and sizes. DHM, known for its precision in representing human bodies virtually and integrating anthropometric data, including ergonomic measurements, enhances the shopping experience by providing valuable insights. Consumers gain access to the knowledge necessary for making tailored clothing choices, thereby enhancing the personalization and satisfaction of their shopping experience. The incorporation of e-library resources takes the garment design approach to a data-driven and customer-centric level. Manufacturers can draw upon a wealth of information regarding body-size diversity, fashion trends, and customer preferences, all sourced from e-libraries. This knowledge supports the creation of a diverse range of sizes and styles, promoting inclusivity and relevance. Beyond improving garment fit, this comprehensive integration streamlines design and production processes by reducing the reliance on physical prototypes. This not only enhances efficiency but also contributes to environmental responsibility, fostering a more sustainable and eco-friendly future for the garment industry and embracing the future of fashion, where technology and data converge to create clothing that authentically fits, resonates with consumers, and aligns with the principles of sustainability. This study developed the mobile application integrating with the information in cloud database in order to present the best-suited garment for the user.