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The purpose of this paper is to focus on how to automatically generate the individualized patterns for women’s suits based on the 3D body point-cloud images.

With the software Imageware, the point-cloud data of the female body were measured according to the female body feature to obtain the heights, widths, depths and girths at various landmarks. Then the relationship between the height of each landmark and the body height was analyzed to build the height calculation rules by software SPSS, and the prediction models of body girths were established from the body widths and depths using regression analysis for pattern generation.

The pattern generation rules were built with the relationships between a human body and the garment patterns using the graphic flattening method. Based on the above rules, the final patterns were drafted automatically by using these dimensions to fit the subjects. The try-on experiment also showed that the individualized suits could fit the subjects’ body well at some feature landmarks.

In order to realize tailor-made and meet the consumers’ demands for individualized clothes, the development of garment CAD system has become inevitable in the garment industry. This paper could provide the foundation for automatic pattern generation, and technical support for tailor-made.

The purpose of this paper is to propose an interactive 2D–3D garment parametric pattern-making and linkage editing scheme that integrates clothing design, simulation and interaction to design 3D garments and 2D patterns. The proposed scheme has the potential to satisfy the individual needs of fashion industry, such as precise fit evaluation of the garment, interactive style editing with ease allowance and constrained contour lines in fashion design.

The authors first construct a parametric pattern-making model for flat pattern design corresponding to the body dimensions. Then, the designing 2D patterns are stitched on a virtual 3D mannequin by performing a virtual try-on. If the customer is unsatisfied after the virtual try-on, the adjustable parameters (appearance parameters and fit parameters) can be adjusted using the 2D–3D linkage editing with hierarchical constrained contour lines, and the fit evaluation tool interactively provides the feedback.

The authors observed that the usability and efficiency of the existing garment pattern-making method simplifies the garment pattern-making process. The authors utilize an interactive garment parametric flat pattern-making model to generate an individualized garment flat pattern that effectively adjust and realize the local editing of the garment pattern-making. The 2D–3D linkage editing is then employed, which alters the size and shape of garment pattern for a precise human model fit of the 3D garment using hierarchical constrained contour lines. Various instances have validated the effectiveness of the proposed scheme, which can increase the reusability of the existing garment styles and improve the efficiency of fashion design.

First, the authors do not consider the garment pattern-making design of sophisticated styles. Second, the authors do not directly consider complex garment shapes such as wrinkles, folds, multi-layer models and fabric physical properties.

The authors propose a pattern adjustment scheme that uses the 3D virtual try-on technology to avoid repetitions of reality-based fit tests and garment sample making in the designing process of clothing products. The proposed scheme provides interactive selections of garment patterns and sizes and renders modification tools for 3D garment designing and 2D garment pattern-making. The authors present the 2D–3D interactive linkage editing scheme for a custom-fit garment pattern based on the hierarchical constraint contour lines. The spatial relationship among the human body, pattern pieces and 3D garment model is adequately expressed, and the final design result of the garment pattern is obtained by constraint solving. Meanwhile, the tightness tension of different parts of the 3D garment is analyzed, and the fit and comfort of the garment are quantitatively evaluated.

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.

With the rapid advancement of computer information technology, the traditional clothing industry has stridden towards automation and digitization that drive the growth of electronic commerce and line retailing. The purpose of this paper is to propose an approach on 3D upper body modelling based on the body measurements extracted by non-contact anthropometry.

Based on the frontal and side images of the human body, the body sizes were extracted through silhouette extraction, identification of landmarks and girth prediction. The generation rules of 15 characteristic cross-sectional curves were established using a method “feature points – inserted points – feature curves – basic surface – mannequin”. The feature points of each position were determined at each curve, such as the side neck point, front neck point, shoulder point, bust point, and bust root point and so on to get the cross-sections, and then some feature points were inserted at the curves according to the widths and depths to establish the calculative models. For example, there are 18 points distributed at the bust cross-sectional curve to determine the shape.

The final mannequin could describe the basic characteristics of a human body, and the shape of the feature curves could also fit the body type to provide basis for the future research on automatic pattern generation.

This study can realize the 3D virtual modelling of female upper body and the automatic generation of the individualized apparel patterns based on the frontal and side images.

This study focused on how to realize automatic recognition of young women's neck-shoulder shape based on the front and side images.

The reverse engineering software was used to measure the body sizes of the neck-shoulder part based on the young women's three-dimensional (3D) point cloud data, and the important parameters closely related to the neck-shoulder shape were determined. The neck-shoulder shape of the subjects was classified to establish the classification rules. Then, based on the front and side images, the human body contour was extracted by Matlab, and the data required for neck-shoulder shape classification were obtained by identifying the feature points.

Through the cluster analysis based on the shoulder angle, back angle, shoulder depth/width ratio and armpit depth/width ratio, young women's neck-shoulder shape was divided into four categories, namely round wide shoulder, flat narrow shoulder, round drop shoulder and hunchback flat shoulder. The neck-shoulder shape could be automatically recognized based on the established classification rules and two-dimensional (2D) body measurement method, with an accuracy rate of 90%. The neck-shoulder shape automatic recognition system constructed based on this method is effective.

This study proposed a simple neck-shoulder automatic recognition method based on the 2D body images. This approach can be extended to other group of human body or other parts of the body.

The purpose of this paper is to focus on solving a fit problem associated with female pants by taking into account the body shape of crotch curves. The patterns of customized pants could be altered with the distance ease (DE) distribution along the crotch curve.

Four pairs of pants with different crotch ease allowances were designed based on a standard mannequin, and used to study how the DE was distributed along the crotch curve at a given ease allowance. The unclothed mannequin and the four pants, which were dressed, respectively, on the mannequin, were scanned consecutively by a body imaging system. The crotch curve of the unclothed mannequin was superimposed on that of each clothed mannequin to exhibit the differences in radial distance so that the DE distribution could be measured.

Through the regression analysis, the prediction models were established to express the relationships between the DE and the ease allowance. These models could be used to estimate the DEs along a crotch curve to reflect its asymmetrical shape when a total allowance was selected. The crotch curves on the pant patterns could be then modified by adding the predicted DEs to the scanned crotch curve.

This study demonstrated a new pattern alteration approach to achieve a better fit for customized female pants based on the 3D scanning data. This approach can be extended to pattern alterations for men’s pants and other shape-critical products.

The goal of this study was to realize pattern alterations for women’s suits by using the spatial distribution of distance ease in the body-garment interface.

An unclothed mannequin and the mannequin clothed with seven suits having different ease allowances were scanned by a 3D body scanner respectively. The image of the unclothed mannequin was then superimposed on that of each clothed mannequin (suit) to exhibit the differences in ease distribution among these suits. The distance eases at ten selected body landmarks were determined by measuring the gaps between the body and suit surfaces.

The mathematical models of ease distributions were built through the regression analysis to predict the distance ease with a given ease allowance. After the verification with the actual measurements, these ease distribution models could provide localized distance eases for alternating pattern pieces to ensure a specified ease allowance.

In order to realize the automatic generation of garment patterns, the ease distribution between a human body and a garment is crucial because ease is one of the determinants for garment fit. This study demonstrated a new approach of automatic pattern alteration based on 3D scanned data to accelerate the pattern making process for women’s suits with customized ease allowance.

The purpose of this paper is to focus on the determination of distance ease of pants from the 3D scanning data of a clothed and unclothed body.

A human model whose body size conformed to the Chinese dummy standard and four pairs of suit pants were chosen for the study. The scanned surfaces of both the body and the pant were superimposed based on the preset markers. The circumferences at four important positions – abdomen, hip, thigh and knee – were selected for pant ease determination. At one position (e.g. hip), the two cross-sections were divided into several characteristic sections and the distance ease, i.e. the space between the cross-sections at each section was measured. The regression equations between the distance ease and ease allowance were then derived so that the distance ease can be estimated.

The relationship was found between the distance ease and the ease allowance. Meanwhile, a mathematic model was established to convert the distance ease into the increments of a pant pattern, which helps to develop an individual pant pattern automatically.

The paper provided the concept and the method to customize a pant by using the 3D scanning data of body. It created a link between the 3D distance ease and the 2D ease allowance, and the model to calculate the distance ease increments which warrant proper ease distributions. The method helps to develop an individualized garment pattern automatically from a basic and tight pant pattern.

Understanding the relationship between the distance ease and the ease allowance and increments of pattern could help develop an individual apparel pattern from 3D measurements. This paper showed a way to solve the problem of distribution of the apparel ease in a virtual environment and convert body measurements from a 3D scanner into personalized apparel patterns.