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Manufacturers have been struggling to meet the wants and needs of their customers without sacrificing the efficiencies and profits gained through mass production. Fortunately, developments in information technology have increased the probability of mass customization being adopted as an acceptable business paradigm. Almost every CAD system used in apparel patternmaking has some method that would enable mass customization through automatic alteration of patterns based on individual measurements. Although each has created an interface somewhat differently from all of the others, most have a number of preparatory activities in common that will allow “automatic” alterations to occur. This article outlines the activities involved in setting up CAD systems to automatically customize garments for fit.

Prior to implementing computerised pattern alterations for figuration on a commercial CAD system, a large database with reference to the standard graded patterns, the alteration movements and relevant information needs to be generated. There are many difficulties in creating grade rules and alteration movements, especially for the bust suppression and the shaped sleeve. In order to obtain sophisticated patterns with a good fit for MTM (made‐to‐measure) clothing, the authors' pattern alteration technique is a combination of pattern design construction and grading. The procedure is divided into ten stages and the practical verification was carried out in the form of seven case studies. In addition, drafting rules on the MicroFit system for automatic pattern generation are demonstrated by converting the numerical grade rules created on the GGT AccuMark system into body‐related formulae.

The paper aims to provide an overview of the area of digital pattern developing for customized apparel.

The paper outlines several methods of digital pattern developing for customized apparel, and discusses the principles, characters and applications. Digital pattern developing process has two paths. One path develops apparel according to traditional 2D pattern‐making technology. There are three methods: parametric design, traditional grading technique, and pattern generating based on artificial intelligence (AI). Another path develops pattern through surface flattening directly from individual 3D apparel model.

For parametric method, it can improve greatly the efficiency of pattern design or pattern alteration. However, the development and application of parametric Computer‐Aided‐Design (CAD) systems in apparel industry are difficult, because apparel pattern has fewer laws in graphical structure. For grading technique, it is the most practical method because of its simple theory, with which pattern masters are familiar. But these methods require users with higher experience. Creating expert pattern system based on AI can reduce the experience requirements. Meanwhile, a great deal of experiments should be conducted for each garment with different style to create their knowledge databases. For 3D CAD technology, two methods of surface flattening have been outlined, namely geometry flattening and physical flattening. But many improvements should be done if the 3D CAD systems are applied in apparel mass customization.

The paper provides information of value to the future research on developing a practical made‐to‐measure apparel pattern system.

The purpose of this paper is to develop an automated custom-pattern-making system using the width-height independent grading method for semi-customized clothing.

The system reads basic patterns and graded large patterns and calculates the grading ratio of independent horizontal and vertical values on all points in a pattern. The custom pattern is automatically generated by calculating the horizontal and vertical grading amount according to individual body measurements. The system does not follow any complicated alteration rules.

The width-height independent grading method can provide custom-pattern fitted primary body circumference and length and helps to quickly produce semi-customized clothing.

There are few studies on automated custom-pattern-making systems without an alteration rule. This study developed an automatic custom-pattern-making system using the width-height independent grading method.

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 concentrate on the development of individualized prototype of apparel patterns for young females from 3D body scanning data.

The authors presented a new pattern-making approach that is composed of three major steps: to establish the relationships between body features and corresponding elements in a prototype (e.g. curve or a point); to classify the relationship into grades that provide alternatives to fit a variety of bodies; and to assemble each individual element into a personalized prototype.

The experiment demonstrated that this method could be used for customized prototype development from 3D body scanning in a relatively easy way.

Currently, the subjects of this study included only Chinese young females, and the regression models were just suitable for the similar body types though, the research method could be extended to other somatotypes and age groups.

This approach can be used in the field of made-to-measure, mass customization, and the quick response for apparel pattern making. The technology in this paper facilitates to generate an individualized pattern prototype from 3D body scanning data.

Originated from the relationship between the features of a human body and the elements of a pattern prototype, the authors presented a new approach to develop an individualized pattern prototype by classifying the features into grades.

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.

Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.

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 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.