• Journal of the Korean Society of Costume
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• v.62 no.6
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• pp.139-153
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• 2012

To prolong the rapid progress of IT, it is necessary to develop contents through IT convergence among the existing goods & service and process areas to create new added-values. In particular, the wedding dress industry has infinite potential in utilizing various contents like virtual fitting by connecting with newly compelling IT areas such as smart phones, Augmented Reality (AR), and application contents. In the meantime, a large scale of the wedding industry has gained global competitiveness due to consulting expertise and the influence of the Korean Wave, whereas most small-sized wedding dress shops in Korea fall short of developing wedding dress designs and receiving relevant information. Accordingly, the purpose of this study was to help brides who have difficulties in choosing a wedding dress by decreasing their time and effort by providing wedding dress designs and information, according their desired image, body type, and circumstances through the utilization of virtual fitting application contents. Not only that, this study aims to diversify and specialize in wedding information and to help users to set a guideline for wedding dresses that are most suitable for them. Moreover, this study has an academic meaning in proposing an interdisciplinary convergence research model through the study of wedding dress design development, AR, and application contents utilization.

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