Kaixuan Liu

Virtual reality-based collaborative design method for designing customized garment for disabled people with scoliosis

Purpose The purpose of this paper is to present a new collaborative design-based method for designing customized garments, aimed at the physically disabled people with scoliosis. Design/methodology/approach The proposed method is based on the virtual human model created using a 3D body scanner, permitting to simulate the consumer’s morphological shape with atypical physical deformations. Next, customized 2D and 3D virtual garment prototyping tools will be used to create products through interactions between the consumer, designer and pattern maker. The general principle of the proposed design method is based on the following sequence: design-display-evaluation-adjustment. After running the sequence for a number of times, the final design solution, which will be approved by both the designer and consumer, can be easily identified. Findings Design knowledge, which is already applied to normal body shapes successfully can be applied to 3D garment design using the concept which is based on collaborative design. Through this process, the classical 2D garment design knowledge, especially 2D patterns and design rules, can be modified and applied according to a normalized virtual garment sensory evaluation procedure quantitatively. This evaluation procedure, interactively performed by the designer and consumer, can permit to adapt the finished product to disabled people afflicted with severe scoliosis. The proposed method is also validated to be more advanced compared to 2D-to-3D virtual CAD design method, especially for atypical morphologies. Originality/value As a co-design method, 3D virtual draping and sensory evaluation can fully satisfy the interaction between the garment design technical space and perceptual space of the finished garments ensuring desired 3D garment fit effect by adjustment of technical parameters. 3D scanning technology is used to generate a complete digitalized 3D human model, permitting to extract the main features of body shapes without accurate measurements. As a knowledge-based design process, both the fashion design knowledge and the pattern making knowledge will be extracted to provide inspirations and references. Successful design solutions will be incorporated into the fashion design knowledge base in order to generate new design rules and enhance professional design knowledge.

Wearing comfort analysis from aspect of numerical garment pressure using 3D virtual-reality and data mining technology

Purpose The purpose of this paper is to find out the main factors that influence wearing comfort and how they influence garment-wearing comfort. Design/methodology/approach Overall, 120 postures were extracted from the activities of daily life and work. Then, the numerical values of clothing pressure of these postures were measured using three-dimension virtual-reality technology. Finally, the data mining technology was applied to analyze the collected data. Findings The wearing comfort of pants is mainly influenced by four factors – waist-hip factor, knee-shank factor, crotch factor and thigh-calf factor – and their contributions account for 39.17, 16.4, 13.96 and 6.95 percent, respectively. Hip, waist, crotch and knee influence wearing comfort significantly, and the part below the knee and the part of back thigh have no obvious effect on wearing comfort. Furthermore, the wearing comfort is acceptable if the numerical clothing pressures are below 20 kPa at the parts of hip, waist and crotch and below 10 kPa at the parts of back thigh, knee and shank. Originality/value The paper demonstrates how different human body parts influence garment-wearing comfort. All of the results in this research facilitate pattern design of pants and quantitative evaluation of garment-wearing comfort.

Design and evaluation of personalized garment block design method for atypical morphology using the knowledge-supported virtual simulation method

This research puts forward a novel knowledge-supported design process for obtaining personalized ready-to-wear garment products aimed at consumers with atypical morphology by using a virtual a three-dimensional-to-two-dimensional (3D-to-2D) design method. The proposed design process starts with designing a personalized garment block, which is then extended into the desired ready-to-wear garment style. The garment block is obtained by using a virtual 3D-to-2D design method. The extension and sizing of the garment block pattern into desired ready-to-wear garment patterns is performed in a 2D environment using classic methods. The proposed design solution begins with a personalized garment block, thus avoiding the complicated operations of simulating a 3D garment directly in the virtual environment. The proposed design process can be fully digitalized, which ensures the involvement of the consumer throughout the design. By repeatedly running the sequence of Design – Display – Evaluation – Adjustment, which is technically accomplished through the virtual 3D-to-2D design method and 3D virtual try-on, the designers’ patterns and principles of elaborating personalized garment products can be validated within a very short time. As a knowledge-supported design process, designers’ design ideas and principles of personalized design solution can be fully extracted to enhance satisfaction of the final product. A set of experiments and case studies validated the approach and application of the proposed knowledge-supported virtual design process, and demonstrated its efficiency.

Construction of a prediction model for body dimensions used in garment pattern making based on anthropometric data learning

Abstract Using artificial intelligence to predict body dimensions rather than measuring them physically is a new research direction in apparel industry. If implemented, this technology can reduce costs and improve efficiency. In this paper, we proposed a back propagation artificial neural network (BP-ANN) model to predict pattern making-related body dimensions by inputting few key human body dimensions. In order to construct the proposed model, anthropometric measurements of 120 young females from the northeastern region of China were collected. The data were then used for training and the proposed model. The results showed that the prediction of the developed BP-ANN model is more accurate and stable than that of linear regression (LR) model. As great as the LR model was at pattern making, the BP-ANN model is even better. In the future, the precision of the proposed model can be further improved if the size of the learning data increases. The proposed method can be especially useful in making garment pattern for form-fitting clothing.

Optimization design of cycling clothes’ patterns based on digital clothing pressures

Enormous research has focused on the analysis of garment wear-comfort using clothing pressure; however, optimization of clothing pressure based garment comfort has remained elusive. In this context, we propose a new method to optimize cycling clothes’ patterns based on the difference of static-to-dynamic clothing pressure (DSDCP). Firstly, we mapped 53 measuring points on an upper cycling garment on which we measured garment pressures in both static and dynamic conditions. We then analyzed DSDCP to find the rightful garment patterns to adjust according to the analyzed results. A garment optimization degree (OD) is proposed to carry out a quantitative analysis for garment comfort optimization. Finally, two upper cycling garments were made according to the original patterns and optimized patterns. A comparative analysis through cyclist wear trials of the cycling garments to test the optimization effect was done. Results show that our proposed method improves dynamic wear comfort significantly. Moreover, the optimized upper cycling garment, offers additional improvement of dynamic wear comfort.

Fit evaluation of virtual garment try-on by learning from digital pressure data

A remote garment fit evaluation model using machine-learning technique is proposed to estimate garment fit without any real try-on.Digital clothing pressures, generated from a 3D garment CAD software, were taken into account during the remote garment fit evaluation.Our proposed model has significance in garment e-shopping. Presently, garment fit evaluation mainly focuses on real try-on, and rarely deals with virtual try-on. With the rapid development of E-commerce, there is a profound growth of garment purchases through the internet. In this context, fit evaluation of virtual garment try-on is vital in the clothing industry. In this paper, we propose a Naive Bayes-based model to evaluate garment fit. The inputs of the proposed model are digital clothing pressures of different body parts, generated from a 3D garment CAD software; while the output is the predicted result of garment fit (fit or unfit). To construct and train the proposed model, data on digital clothing pressures and garment real fit was collected for input and output learning data respectively. By learning from these data, our proposed model can predict garment fit rapidly and automatically without any real try-on; therefore, it can be applied to remote garment fit evaluation in the context of e-shopping. Finally, the effectiveness of our proposed method was validated using a set of test samples. Test results showed that digital clothing pressure is a better index than ease allowance to evaluate garment fit, and machine learning-based garment fit evaluation methods have higher prediction accuracies.

Collaborative 3D-To-2D Tight-Fitting Garment Pattern Design Process For Scoliotic People

This paper presents a virtual design process for a tight-fitting garment pattern for adapted consumer garments, aimed at consumers with scoliosis. The design process proposed is based on a virtual human model created using a 3D scanner, allowing simulation of the morphological shape of an individual with atypical physical deformations. Customized 2D and 3D virtual garment prototyping tools are used in combination to create products through interactions between the consumer, designer and pattern maker. After following an interactive sequence: Scanning – Design – Display – Evaluation – Adjustment, a final design solution acceptable to both the designer and consumer can be obtained. Through this process, traditional 2D garment design knowledge, especially design rules influenced by the fabric information, is fully utilized to support the design process proposed. Using the knowledge based collaborative design process, design satisfaction can be largely improved.

Visual-Simulation-Based Personalized Garment Block Design Method for Physically Disabled People with Scoliosis (PDPS)

Abstract This research presented a novel method using 3D simulation methods to design customized garments for physically disabled people with scoliosis (PDPS). The proposed method is based on the virtual human model created from 3D scanning, permitting to simulate the consumer’s morphological shape with atypical physical deformations. Next, customized 2D and 3D virtual garment prototyping tools will be used to create products through interactions. The proposed 3D garment design method is based on the concept of knowledge-based design, using the design knowledge and process already applied to normal body shapes successfully. The characters of the PDPS and the relationship between human body and garment are considered in the prototyping process. As a visualized collaborative design process, the communication between designer and consumer is ensured, permitting to adapt the finished product to disabled people afflicted with severe scoliosis.

A mixed human body modeling method based on 3D body scanning for clothing industry

Purpose The purpose of this paper is to propose a relatively simple and rapid method to create a digital human model (DHM) to serve clothing industry. Design/methodology/approach Human body’s point cloud is divided into hands, foots, head and torso. Then forward modeling method is used to model hands and foots, photo modeling method is used to model head and reverse modeling method is used to model torso. After that, hands, foots, head and torso are integrated together to get a static avatar. Next, virtual skeleton is bound to the avatar. Finally, a lifelike digital human body model is created by the mixed modeling method (MMM). Findings In allusion to the defect of the three-dimension original data of human body, this paper presented an MMM, with which we can get a realistic digital human body model with accurate body dimensions. The DHM can well meet the needs of fashion industry. Practical implications The DHM, which is got by the MMM, can be well applied in the field of virtual try on, virtual fashion design, virtual fashion show and so on. Originality/value The originality of the paper lies in the integration of forward modeling, reverse modeling and photo modeling to present a novel method of human body modeling.

3D interactive garment pattern-making technology

Abstract The traditional pattern-making process is very time-consuming and requires professional fashion design knowledge. In order to develop a form-fitting garment to meet customer’s individual needs, pattern makers must rely on a “trial and error” procedure until the customer is satisfied. In this paper, we proposed a “what you see is what you get” (WYSIWYG) way to efficiently develop garment patterns. First, a three-dimensional (3D) garment, using an extracted outline from a garment flat or figure, is modeled in a gravitational virtual environment. The modeled garment is then adjusted until it meets design requirements. Next, the adjusted 3D garment model is expanded by smoothing out the folds and wrinkles. Construction curves are drawn on the surface of the expanded 3D garment model according to design requirements. These curves divide the 3D garment model’s surface into many small 3D surfaces. Then, 2D garment patterns are obtained by unfolding these subdivided 3D surfaces. Finally, the flattened 2D patterns are stretched and shrank according to the fabric elasticity. The final patterns can be used for making real garments. Compared to the current 3D garment pattern-making methods, our proposed method is more robust and well-rounded; not only is the proposed approach versatile towards both tight-fitting and loose-fitting clothing, but also requires no prior knowledge of pattern-making from the user. It also involves garment ease allowance, fabric elasticity, and draping, three factors that had not been previously considered all at once during smart pattern-making procedures, in the designing process.