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The research evaluated the feasibility of 3D dynamic fit utilizing female compression tops by comparatively analyzing the virtual and actual dynamic fit.

Six female participants were 3D body-scanned and photographed in compression tops in four types of athletic movements (pull-up, kettlebell swing, circle-crunch and sit-up). Fit measurements, waist cross-sectional areas, waist width, waist depth, numerical simulation of clothing pressure (kPa) and objective pressure measurements (kPa) were collected from 3D virtual animation, 3D fit scan data and actual photos with the four types of athletic motions. The data were comparatively investigated between virtual and actual dynamic fit.

The 3D-animated body was not reflected with human body deformation because only bone structure was changed while maintaining the constant forms of muscle and body surface in athletic movements. Due to this consistency of virtual dynamic fit, there were significant differences with the actual dynamic fit at the top length, shoulder width and waist cross-sectional areas. Also, the virtual dynamic pressure indicated significantly higher levels than the objective dynamic pressure while presenting no significant correlations at the front neckline, breast, lateral waist, upper back, back armhole and back waist.

This study is the first to verify multiple aspects of virtual dynamic fit using 3D digital technology. This study provided useful information about which aspects of the current virtual animation need to be improved to apply in the dynamic fit evaluation.

This research aimed to develop an automatic 3D body measurement line generation method that reduces errors induced by diverse body shapes and incomplete scan areas.

Three-dimensional body scan data from the 5th Size Korea database were used. Measurement extraction methods were developed for each measurement; chest girth, underbust girth, armscye girth and neck base girth.

The research showed that the method adopted in this study enhanced the accuracy of the scan measurements for various body shapes and incomplete scan data. The authors verified the accuracy of the developed methods for various body shapes by comparing the scan measurement against manual measurement.

The automatic 3D body measurement line generation algorithms developed for various human body shapes will improve the reliability and accuracy of 3D body scan measurement program. Also. it will be of practical use in human-size-related production processes.

Accurate and automatic clothing pattern making is very important in personalized clothing customization and virtual fitting room applications. Clothing pattern generating as well as virtual clothing simulation is an attractive research issue both in clothing industry and computer graphics.

Physics-based method is an effective way to model dynamic process and generate realistic clothing animation. Due to conceptual simplicity and computational speed, mass-spring model is frequently used to simulate deformable and soft objects follow the natural physical rules. We present a physics-based clothing pattern generating framework by using scanned human body model. After giving a scanned human body model, first, we extract feature points, planes and curves on the 3D model by geometric analysis, and then, we construct a remeshed surface which has been formatted to connected quad meshes. Second, for each clothing piece in 3D, we construct a mass-spring model with same topological structures, and conduct a typical time integration algorithm to the mass-spring model. Finally, we get the convergent clothing pieces in 2D of all clothing parts, and we reconnected parts which are adjacent on 3D model to generate the basic clothing pattern.

The results show that the presented method is a feasible way for clothing pattern generating by use of scanned human body model.

The main contribution of this work is twofold: one is the geometric algorithm to scanned human body model, which is specially conducted for clothing pattern design to extract feature points, planes and curves. This is the crucial base for suit clothing pattern generating. Another is the physics-based pattern generating algorithm which flattens the 3D shape to 2D shape of cloth pattern pieces.

This study aims to investigate if engineered compression variations using moisture-responsive knitted fabric design can improve breast support in seamless knitted sports bras.

An experimental approach was used to integrate a novel moisture-responsive fabric panel into a seamless knitted bra, and the resulting compression variability in dry versus wet conditions were compared with those of a control bra. Air permeability and elongation testing of between breasts fabric panels was conducted in dry and wet conditions, followed by three-dimensional body scanning of eight human participants wearing the two bras in similar conditions. Questionnaires were used to evaluate perceived comfort and breast support of both bras in both conditions.

Air permeability test results showed that the novel panel had the highest variance between dry and wet conditions, confirming its moisture-responsive design, and increased its elongation coefficient in both wale and course directions in wet condition. There were significant main effects of bra type and body location on breast compression measurements. Breast circumferences in the novel bra were significantly larger than in the control bra condition. The significant two-way interaction between bra type and moisture condition showed that the control bra lost compressive power in wet condition, whereas the novel bra became more compressive when wet. Changes in compression were confirmed by participants’ perception of tighter straps and drier breast comfort.

These findings add to the limited scientific knowledge of moisture adaptive bra design using engineered knitted fabrics via advanced manufacturing technologies, with possible applications beyond sports bras, such as bras for breast surgery recovering patients.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

Body shape is a critical variable influencing consumers' garment choices (Zakaria, 2017), yet research investigating how UK females with varying body shapes evaluate and experience fit is limited. Moreover, while digital methods exist to classify female body shapes, application in a commercial setting is limited. To fill this gap within the literature, this study aims to understand the influence of body shape variation on garment fit evaluations of 30 UK females aged 18–34 years.

This study conducts a physical garment try-on session whereby 30 UK females aged 18–34 years were body-scanned and categorised into a body shape, using the Female Figure Identification Technique method. Participants verbalised their fit experiences during a physical try-on session through semi-structured interviews to gain a deeper understanding of individuals' fit appraisals.

The findings demonstrate that UK females who share the same body shape classification experience the same issues when appraising dress fit, challenging Makhanya and Mabuza (2020) who found that body shape does not influence apparel fit satisfaction. The results shed light on the importance of body shape during the fit appraisal process.

Despite the contribution, future research to improve the limitations should be addressed. First, although it was necessary to investigate a UK demographic to address research gaps, the finding of this study cannot be generalised to the entire UK female population nor to other areas of the world. Hence, future research should overcome this limitation by extending this study further to other countries, cultures and ethnicities.

The findings of the present study shed light on the role of body shape in determining satisfactory clothing fit and how females' fit experiences will differ depending on their body shape classification. Thus, fashion retailers should use this insight to better inform their promotional strategies, not only making them more inclusive but also to help assist this particular consumer segment with their clothing decisions based on their body shape.

The present study provides an in-depth understanding of how females with the same body shape experience garment fit, contributing novel findings to the literature through a mixed-method inquiry previously lacking in this area, with a UK demographic which has not previously been explored.

Virtual try-on (VTO) technology with three-dimensional (3D) body scanning in a mobile application is a relatively new technique for selling custom-fit apparel. VTO involves scanning and measuring one's body and visualizing the fit of a garment on a 3D avatar. The purpose of this study is to explore consumers' experiences toward the custom-fit T-shirts and online mass customization (MC) services using the VTO technology found in online consumer reviews (OCRs).

A total of 297 OCRs were collected from the Amazon's Made for You site that uses VTO technology for the MC process. A qualitative content analysis, within a mixed method research process, was used to determine systematically the meanings within qualitative data with quantitative results. In the qualitative approach, combinations of two coding processes were employed, which were concept-driven (i.e., deductive/a priori) and data-driven (i.e., inductive/emergent) coding processes. In the quantitative approach, the prevalence of each coding in terms of its valence was calculated based on frequencies. Intercoder reliability reached 96 per cent.

In OCRs of customized apparel products and online MC services using VTO technology, consumers described expectations, perceived performance, dis/confirmation, dis/satisfaction, outcomes of dis/satisfaction and descriptive information. Those with expectations often expressed skepticism about the product and the MC process using VTO technology at the pre-consumption stage. In OCRs, they used four product dimensions and two service dimensions of perceived performance. Consumers had positive (negative) confirmation when the performance of the T-shirts and/or services worked better (worse) than their expectations. The OCRs also included dis/satisfaction with a product and/or service, its outcomes and descriptive information.

This study identified a resulting framework to identify the content in OCRs of the custom-fit T-shirts and online MC services that use VTO technology. This study extends the expectation confirmation theory by adding multiple dimensions (i.e., four product dimensions and two service dimensions) as well as more outcomes of dis/satisfaction (not limited to repurchase intentions). This study provides practical suggestions for online MC companies who are using or planning to use VTO technology on how to improve consumers' satisfaction with customized T-shirts using VTO technology.

The purpose of the present study was to improve the fit of women’s bifurcated garments by developing an equation that can predict the crotch length accurately by using a few basic body measurements. This equation could provide a simple mass-customization approach to the design of bifurcated garments.

Demographic characteristics and easy-to-record body measurements available in the size USA database were used to predict the crotch length. Different methodologies including best subset regression, lasso regression and principal components regression were experimented with to identify the most important predictor variables and establish a relationship between the significant predictors and crotch length.

The lasso regression model provided the highest accuracy, required only five body dimensions and dealt with multicollinearity. The preliminary pattern preparation and garment fit tests indicated that by utilizing the proposed equation, patterns of customized garments could be successfully altered to match the crotch length of the customer, thereby, improving the precision and efficiency of the pattern making process.

Crotch length is a crucial measurement as it determines bifurcated garment comfort as well as aesthetic fit. The crotch length is usually estimated arbitrarily based on non-scientific methods while drafting patterns, and this increases the likelihood of dissatisfaction with the fit of the lower-body garments. The present study suggested an algorithm that could predict crotch length with 90.53% accuracy using the body dimensions height, hips, waist height, knee height and arm length.

3D printing has been warmly welcomed by clothing enterprises for its customization capacity in recent years. However, such clothing enterprises have to face the digital transformation challenges brought by 3D printing. Since the business model is a competitive weapon for modern enterprises, there is a research gap between business model innovation and digital transformation challenges for 3D-printing garment enterprises. The aim of the paper is to innovate a new business model for 3D-printing garment enterprises in digital transformation.

A business model innovation canvas (BMIC), a new method for business model innovation, is used to innovate a new 3D-printing clothing enterprises business model in the context of digital transformation. The business model canvas (BMC) method is adopted to illustrate the new business model. The business model ecosystem is used to design the operating architecture and mechanism of the new business model.

First, 3D-printing clothing enterprises are facing digital transformation, and they urgently need to innovate new business models. Second, mass customization and distributed manufacturing are important ways of solving the business model problems faced by 3D-printing clothing enterprises in the process of digital transformation. Third, BMIC has proven to be an effective tool for business model innovation.

The new mass deep customization-distributed manufacturing (MDC-DM) business model is universal. As such, it can provide an important theoretical reference for other scholars to study similar problems. The digital transformation background is taken into account in the process of business model innovation. Therefore, this is the first hybrid research that has been focused on 3D printing, garment enterprises, digital transformation and business model innovation. On the other hand, business model innovation is a type of exploratory research, which means that the MDC-DM business model’s application effect cannot be immediately observed and requires further verification in the future.

The new business model MDC-DM is not only applicable to 3D-printing garment enterprises but also to some other enterprises that are either using or will use 3D printing to enhance their core competitiveness.

A new business model, MDC-DM, is created through BMIC, which allows 3D-printing garment enterprises to meet the challenges of digital transformation. In addition, the original canvas of the MDC-DM business model is designed using BMC. Moreover, the ecosystem of the MDC-DM business model is constructed, and its operation mechanisms are comprehensively designed.

This study focused on how to quantify the similarities of body shape based on the front and side images, and a shape comprehensive index (I_SC) of female upper body shape based on 2D images was proposed.

In total, 190 young women were shot for front and side images, and 18 shape parameters were automatically extracted, including seven angles and 11 ratio parameters. The coefficient of variation method was used to assign different weights for related parameters, and the I_SC was calculated to describe the body shape of each subject. Five cross-sectional curves of the upper body (e.g. shoulder, chest, waist, abdomen and hip) were selected for exploring the range of shape similarity.

According to the value of I_SC, if the difference among the subjects is within the range of ±0.02, their body shapes can be regarded as similar, and the subject with the minimum distance is considered as the most similar. Error results show that the error range of the angle parameter is from 0.2° to 3.6° and the ratio range is from 0.001 to 0.119. Moreover, the t-test value among the parameters of the similar body is above 0.05, indicating that there is no significant difference for the upper body shape of the similar groups.

This method can quantify body shapes with the upper body characteristics of young women instead of subjective judgment. The study can be extended to other parts of the body and can also provide a new thought for shape similarity retrieval based on 2D images.