• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.05a
• /
• pp.80-81
• /
• 2022

In this paper, the physical properties of actual fabric data are predicted using the Cusick drape system, which is a means of measuring the physical properties of fabrics. Using a three-dimensional volumetric system, the cloth data of the actual Cusick drape system is acquired in a three-dimensional point cloud format. Cusick drape simulation is performed using mesh data of the same shape and size as the fabric, and the physical parameters of the draped fabric most similar to the actual draped fabric are acquired.

• Journal of Fashion Business
• /
• v.15 no.3
• /
• pp.97-111
• /
• 2011

This study aims to apply 3D virtual fabric parameters - as obtained from previous research experiments - to 3D virtual clothing simulation in comparing its similarity with actual clothing as worn, with a view to verifying the objectivity and validity of the 3D virtual fabric simulation method devised by the drape image analysis method. In addition, the result is intended to be used as the basic data for new 3D virtual clothing simulation methods. As the results, 3D virtual fabric parameters designed to simulate 3D drape to be similar to actual fabrics were found to be Bending Strength, Buckling Point, Density, Particle Distance, and Shear. They were also found to be important measurements when evaluating visual similarity between drape shadow images and number of nodes. 3D virtual fabric simulation method devised by the drape image analysis method was appropriate in extracting 3D fabric parameters with the reflection of actual fabrics' physical and dynamic characteristics, in connection with 3D virtual fabric simulation. 3D virtual fabric parameters with the reflection of actual fabrics' physical and dynamic characteristics using the proposed 3D virtual fabric simulation method are accumulated and provided as a standard, this will facilitate the introduction 3D virtual fabric simulation technology.

• Fibers and Polymers
• /
• v.7 no.4
• /
• pp.436-441
• /
• 2006

An integrated platform for garment drape simulation system has been developed. In this system, garment patterns from conventional two-dimensional CAD systems can be assembled into a three-dimensional garment on a parametrically resizable realistic human body model. A fast and robust particle-based physical calculation engine has been developed for garment shape generation. Then a series of geometric and graphical techniques were applied to create realistic impressions on simulated garments. This system can be used as the rapid prototyping tool for garments in the future quick-response system.

• Journal of the Korea Computer Graphics Society
• /
• v.26 no.5
• /
• pp.15-24
• /
• 2020

We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick's drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a significant influence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we first collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is fitted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes.

• Smart Media Journal
• /
• v.10 no.4
• /
• pp.28-34
• /
• 2021

Drape is one of the factors that determine the shape of clothes and is one of the very important factors in the textile and fashion industry. At a time when non-face-to-face transactions are being activated due to the impact of the coronavirus, more and more companies are asking for drape value. However, in the case of small and medium-sized enterprises (SMEs), it is difficult to measure the drape, because they feel the burden of time and money for measuring the drape. Therefore, this study aimed to generate a drape image for the material property value input using a conditional adversarial neural network through 3D simulation images generated by measuring digital properties. A drape image was created through the existing 736 digital property values, and this was used for model training. Then, the drape value was calculated for the image samples obtained through the generative model. As a result of comparing the actual drape experimental value and the generated drape value, it was confirmed that the error of the peak number was 0.75, and the average error of the drape value was 7.875

• Fibers and Polymers
• /
• v.4 no.4
• /
• pp.169-175
• /
• 2003

A simulation system for versatile garment drape has been developed. Using this system, the shape of a garment can be simulated in consideration of fabric physical properties as well as the interaction between fabrics and other objects. Each fabric piece in a garment is modeled using a geometrically constrained particle system and its behavior is calculated from an implicit numerical integration algorithm in a relatively short time. The system consists of three modules including a preprocessor for the preparation of fabric patterns and external objects, a postprocessor for the results of three-dimensional visualization, and a drape simulation engine. It can be used for the design process of textile goods, garments, furniture, or upholsteries.

• Journal of the Korea Fashion and Costume Design Association
• /
• v.8 no.2
• /
• pp.27-35
• /
• 2006

We studied the 3D simulating shapes of the flared skirt using NARCIS-Drape Simulation software made in Korea D&M FT. The experimental conditions were made of three types of cuttings (lengthwise, crosswise, 45 bias) and polyester fabrics for flared skirt (light, medium, heavy) and different simulating repetitions (1, 2, ${\cdots}$, 9, 10 times). We accomplished some experimental data on the 3D simulating shapes of the flared skirt made by different conditions. The 3D simulation shapes of the flared skirts were gradually getting stabilized from 5 repetitions. And the length of skirts and the width and depth of hems diminished lower by degrees as the simulating repetitions. It is considered that the simulating repetition for the flared skirt was appropriate in the range of 8 to 10 times. But it was not reasonably showed that the difference in the drape of the flared skirt was made by different cuttings and fabrics.

• Journal of the Korea Computer Graphics Society
• /
• v.29 no.3
• /
• pp.117-125
• /
• 2023

In the development of clothing design through virtual simulation, it is essential to minimize the differences between the virtual and the real world as much as possible. The most critical task to enhance the similarity between virtual and real garments is to find simulation parameters that can closely emulate the physical properties of the actual fabric in use. The simulation parameter optimization process requires manual tuning by experts, demanding high expertise and a significant amount of time. Especially, considerable time is consumed in repeatedly running simulations to check the results of applying the tuned simulation parameters. Recently, to tackle this issue, artificial neural network learning models have been proposed that swiftly estimate the results of drape test simulations, which are predominantly used for parameter tuning. In these earlier studies, relatively simple linear stiffness models were used, and instead of estimating the entirety of the drape mesh, they estimated only a portion of the mesh and interpolated the rest. However, there is still a scarcity of research on non-linear stiffness models, which are commonly used in actual garment design. In this paper, we propose a learning model for estimating the results of drape simulations for non-linear stiffness models. Our learning model estimates the full high-resolution mesh model of drape. To validate the performance of the proposed method, experiments were conducted using three different drape test methods, demonstrating high accuracy in estimation.

• Fibers and Polymers
• /
• v.5 no.1
• /
• pp.12-18
• /
• 2004

A parametric body model generation system has been developed. Using various mathematic and geometric algorithms of this system, a three-dimensionally scanned human body can be converted into a resizable body model. Once a parametric body model is formed, its size and shape can be modified instantaneously by providing appropriate anthropometric data. To facilitate the subsequent pattern arrangement process for garment drape simulation, a bounding box generation algorithm has been developed in this study. Also the model can be converted into a set of parametric surfaces that it can also be used for three-dimensional garment pattern design system.

• Fashion & Textile Research Journal
• /
• v.12 no.5
• /
• pp.582-590
• /
• 2010

Garment design needs an iterative manipulation of 2D patterns to generate a final sloper. Traditionally there have been two kinds of design methodologies such as the flat pattern method and the pattern draping method. But today, it is possible to combine the advantages from the two methods due to the realistic cloth simulation techniques. We devised a new garment design system which starts from 3D initial drape simulation result and then modifies the garment by editing the 2D flat patterns synchronously. With this interactive methodology using real-time pattern deformation technique, the designer can freely change a pattern shape by watching its 3D outlook in real-time. Also the final garment data were given relative coordinates with respect to the human anthropometric feature points detected by an automatic body feature detection algorithm. Using the relative human body coordinate system, the final garments can be re-used to an arbitrary body data without repositioning in the drape simulation. A female shirt was used for an example and a 3D body scan data was used for an illustration of the feature point detection algorithm.