The effectiveness of 3D virtual fitting technology when visualizing the fit and silhouette of pants by analyzing the similarities between real and virtual fit using 20 fit locations, 3 lower body shapes, and fit status was investigated. We produced 61 custom pants for the participants and virtually tried each on a personalized 3D body scan avatar. The technology was not generally effective for visualizing pant fit. Especially, the waist placement of the virtual pants was lower than that for the actual pants. The virtual software indicated less ease than the actual pants and could not express stress folds due to slight misfit. The front silhouette of the virtual pants spread wider than the real pants. The virtual pants for females with a hip tilt shape had the greatest divergence from the actual pants. The virtual pants with good fit appeared more effectively than did those with poor fit.

Mass customization and automated custom clothing have been regarded as promising methods for the apparel industry to create well-fitting clothing for customers. However, current off-the-shelf automated custom patternmaking software cannot generate custom clothing with perfect fit since alteration starts from a single graded base pattern regardless of customers' body shape, resulting in an extreme pattern alteration in areas that the system cannot accomplish effectively for some customers. Therefore we developed a set of basic pants patterns optimized for three lower body shape groups, and tested whether improved customization could occur if the alteration process is started from differently shaped block patterns that are suitable for each body figure. The body shape groups were identified using a new data driven method using multiple body measurements (depths, angles, and arcs). The results showed that the new made-to-measure system incorporating body shape information into block patterns can generate custom patterns with better fit.

Body shapes are generally identified by subjective comparisons of body silhouettes or by calculating ratios of girths. In this study, we developed a reliable and objective categorization method for the lower body shapes of women using principal component (PC) analysis and cluster analysis. A total of 2,488 women aged 18—35 within the 90th percentile of body mass index (34.14) were selected from SizeUSA body scan data. Body measurements chosen for the analysis include buttocks angle and 14 proportional measures of widths, depths, front/back depths, girths, and front/back arcs of key lower body locations. Five PCs were extracted, but only the first three components made a strong contribution to explained variance (PC1: waist to top hip silhouette, PC2: top hip to full hip silhouette, and PC3: buttocks prominence). However, PC4 (abdomen prominence) and PC5 (slope from abdomen point to front hip point) were also critical for representing a distinctive shape. They had a single variable, and therefore each variable was retained as z-score. Three body shape groups were categorized by K-means cluster analysis using three PC scores and two z-scores. In order to provide a simple and intuitive application method for defining a new person’s body shape group, we developed two discriminant functions using raw measurements. Body shape can be classified within our system from body measurements by calculating function scores and comparing them with a bivariate plot of function scores of the body shape groups.

3D scans are considered to be potentially useful visual analysis tool, but there have been few studies regarding the validity of 3D scans as a tool for visual analysis. We investigated this by comparing results from fit analyses using live models with those using 3D scans of the same models. Professionals from the apparel industry provided feedback on the process of 3D scan fit analyses and suggested ways to improve the process. Results from different areas of the body showed different levels of reliability. In the bust area, misfit and estimates on alteration amounts were the most reliable, while the waist area showed the least consistency in fit interpretation overall. The hip area was not reliable for identification of misfit, but was relatively reliable for estimates of alteration amounts. This study suggested how future studies could improve the process in terms of data organization, display and the navigation interface.

The objectives of this study are to identify the principal components that represent distinctive shapes from the silhouette and profile views of the lower body shapes of abdominal obese Korean men and to categorize their body types. Using 3D scans of 625 men aged 35–64 in the 6th SizeKorea dataset, 173 scans (27.7%) of men in ‘abdominal obese’ category (BMI value of 25, waist girth to height ratio of 0.53, and waist girth to hip girth ratio of 0.9 or higher) were utilized. We developed a script to measure 38 items such as front/back crotch length and front/back depths and angles using the SNU-BM program, which is a script-based automated 3D body scan measurement software. The measurements used for principal component (PC) analysis were 31 drops, 2 heights, 2 lengths and 4 angles. Ten PCs representing distinctive silhouettes and profiles of lower body shapes were extracted. The PCs were interpreted as follows: abdomen prominence, thigh to knee profile, upper buttocks prominence, waist to hip drop, thigh to knee silhouette, lower body tilt angle, waist to crotch length, vertical height, abdomen to crotch height, and lower buttocks slope. The three body shape groups were categorized using a K means cluster analysis with ten PC scores. Group 1 had a flat abdomen but prominent buttocks. Group 2 had a developed abdomen and buttocks with vertical thighs. Group 3 had drooped buttocks with tilted thighs.