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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.

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.

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.

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.

The purpose of this paper is to build a mathematical model of men’s wear prototype, so that the computer can draw men’s wear prototype automatically.

First, the bust line, front and back center line, waist line, side seam, front chest width and back width line in the coordinate system are expressed by equations. Then, a parabola is used to establish a neckline curve, a linear equation is used to establish a shoulder oblique line, and a double ellipse is superposed to construct the armhole arc. Finally, all the garment prototype curves are built mathematical models.

The result shows that every curve of garment prototype can be expressed approximately by mathematical model.

This research lays the foundation for the automation and intelligence of garment pattern-making.

The study in this article aims to focus on a novel design concept of virtual 3D garment which is realized with the help of traditional patternmaking methodology and the CAD softwares in order to directly conceive the virtual clothing on a mannequin morphotype in cyberspace in consideration of the ease allowance between the body shape and the garment.

The method of acquisition of 3D human body was explained at first. Then the process of creation of garment 3D model associated with the draping technique was presented. The superposition of patterns from the 3D modeling and the traditional method used in industries was done in order to visualize the right results. At last, the dynamic validation of the garment was carried out in order to analyze the fitting results of try‐on simulation.

The 3D modeling technique method based on the draping technique shows that the garment fits perfectly to the body shape of the wearer.

For the ready‐to‐wear manufacture, this method can be also involved on the parametric mannequin in order to reduce the lifetime of development by eliminating the process of pattern grading in the future.

The originality of this article comes from the combination of the traditional draping technique with the advanced CAD softwares in consideration of the fitting and draping of the garment. This concept is used not only in the context of mass customized product but also in mass production for the ready‐to‐wear apparel industries. The patterns are directly adjusted in 3D and can immediately be tried on in 3D simulation. As a result, the process in 2D patternmaking design can be eliminated.

This paper presents an introduction to the modelling of virtual garment design process in 3D… Our global project of virtual clothing design, along with the conception of a virtual adaptive mannequin, is devoted to creating and modelling garments in 3D. Starting from ideas of mass customization, e-commerce and the need of numerical innovations in the garment industry, this article presents a model of virtual garment and methodology enabling virtual clothing to be conceived directly on an adaptive mannequin morphotype in 3D. A short description of the overall garment model under constraints is presented. To explain the overall methodology, the basic pattern of trousers is given. The global model of garment creation in 3D is composed of three parts - a human body model, an ease model and a garment model. The most essential part is the ease model, which is necessary for the proposed process of garment modelling. After describing each garment modelling element influencing this process, a detailed presentation of the ease model in relation to the garment model is proposed. The combination of the previously mentioned models may be considered as 2 interconnected sub-models. The first sub-model is linked with the front pattern position on the body and the second with the back pattern position on the trousers with appropriate ease values. In order to execute the identification procedure of the correct ease values and consequently their right positions on the human body, an algorithm of identification is proposed. The two sub-models are strongly connected as in the feedback effect caused by the interactions of the trouser front and back patterns. The aforementioned connection phenomenon appears during modelling and it depends on the structure of the proposed ease model. The relatively significant number of parameters requires the use of the identification technique. Finally, the superposition of virtual and real patterns was done in order to visualise the results.

The purpose of this paper is to present recent work for optimizing the estimation of ease allowance of a garment using fuzzy logic and sensory evaluation.

The current method first generates a number of fuzzy models each corresponding to one specific key body part and one specific wearer's movement and then aggregates all the values of ease allowance generated from these fuzzy models using the ordered weighted averaging (OWA) operator. The aggregated ease allowance takes into account geometric measures on all representative human bodies, comfort sensations of wearers related to all movements or actions and different styles of trousers (tight, normal and loose). The weights of the OWA operator can be used to adjust the compromise between the style of garments and the comfort sensation of wearers. The related weights of the OWA operator are automatically determined according to designer's linguistic criteria characterizing the relationship between wearer's movements and the features of the garment to be designed.

Based on the optimized values of ease allowance generated from fuzzy models related to different key body positions and different wearer's movements, the authors obtain a personalized ease allowance, permitting to further improve the wearer's fitting perception of a garment. The effectiveness of the method has been validated in the design of trousers of jean type. It can also be applied for designing other types of garment.

Integration of wearer's body shapes and human comfort in the design of personalized garments.

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.

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).

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.

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.

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.

In order to study the static and dynamic comfort of tight sportswear in winter, the subjective comfort was aimed to be evaluated by collecting sensory data such as humidity feeling, cold feeling and other perceptions. In this paper, the experiment was divided into standing, squatting, jumping, jogging, walking and so on.

Through particle swarm optimization-cuckoo search model, the sensory factors that affect the overall comfort were optimized, and it was found that there were great differences in the overall comfort factors under different motions. Then, analytic hierarchy process was used to sort the optimized sensory indicators in each experimental stage, and the influence degree of sensory indicators was studied. Finally, by the long short-term memory (LSTM) model, taking comfort senses of standing, squatting, jumping and jogging as input parameters, and regarding comfort senses of walking, lifting legs and resting as output parameters, the prediction model was founded.

The results showed that there were certain differences between the prediction value and the real subjective evaluation value, but most of the predicted values were consistent with the real values on the sensory level, and the overall prediction level was good, which meant that the LSTM model had more accurate prediction ability for subjective evaluation and could be extended to other sports.

The research results could provide scientific methods for the design of tight-fitting sportswear in winter.

Considering only two-dimensional (2D) ease allowance cannot fully reflect the three-dimensional (3D) relationship between the position of clothing and the human body. The purpose of this paper is to propose a method with a 3D space vector and corresponding distance ease to characterize fitting garments and then used to construct personalized clothing for similar shape body.

Firstly, a 3D scanner was used to obtain mannequin and fitted garment data, and 17 layers of cross-sections of the upper body were extracted. Then, 37 space vectors and corresponding space angles on each cross-section were obtained with the original point. Secondly, the detailed distance ease between the mannequin and garment was constructed due to the difference between garment vectors and body vectors. Thirdly, the distance ease mathematical models were achieved and used to calculate distance ease on a similar shape body. Additionally, the fit garment is constructed, and the garment pattern is altered by the geometric pattern alteration method.

The results show that 3D space vectors can explain the relationship between body skin and garment surface of the upper body properly. The distance ease is modeled by mathematic expressions and successfully used to make a new garment to fit a similar shape body.

The proposed method of constructing garments based on distance ease and 3D space vectors can create a fitted garment for a similar shape body effectively and accurately. It is useful for the personalized garment design and suitable for the manufacturing process.

The purpose of this paper is to develop a dress design knowledge base (DDKB), which is expected to be further applied to a personalized dress recommendation system.

Dress design knowledge can be expressed as the relationship between designer's fashion perceptions of different dress elements. In order to extract dress design knowledge, a dress shape ontology (DSO) is firstly developed, which can be further used to form a dress element matrix (DEM). A perceptual descriptive space of the dress (DPDS) is developed for the description of the designer's fashion perception of dress. Through a standard sensory evaluation procedure performed by experienced experts (designers), the expected relationship can be obtained. This relationship is then mathematically simulated by fuzzy logic tools for the expected DDKB.

In this paper, a DDKB has been developed. The established knowledge base has been validated, and it can be further applied to dress recommendation system for a specific consumer.

This study introduces the concept of knowledge base to the area of dress individualized design. The knowledge-based design process based on sensory evaluation and fuzzy logic can efficiently solve the individualization of dress design in traditional design processes, which can provide a novel way to dress design individualization.