A Method to Develop a Personalized Pattern of Pant from 3D Scanning Data

Jian-jun, SU; Kong, Ying; Gu, Bingfei; Kuang, Caiyuan

doi:10.15221/17.121

Cited by 3 publications

(5 citation statements)

References 13 publications

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“…Hence, a few self-defined measurements were introduced. The concept to reflect the individuality of a body used in this paper was previously mentioned, but not fully explained (as in Figure 2), by Su et al 16 Our paper provides a complete illustration of the lowerbody features in 3D body images (see Figures 2 to 4).…”

Section: Methodsmentioning

confidence: 94%

“…Thus, the ratio of thickness and width was adopted to reflect a feature's individuality as RG feature ¼ thickness of feature width of feature , in which the subscript indicates the relevant feature's name, and the thicknesses and widths features were measured from the front and side views from the corresponding 3D scan data. It should be noted that the general rules on girth developed in our previous work 16 are cited in Tables 5 to 8 in this paper with better illustration and more explanation.…”

Section: Individualized Rules On Girthmentioning

confidence: 92%

“…14,15 The influence of these factors and a method to quantize them for the fit of pants has been explored in two earlier papers. 14,16 In order to push forward our research project, some of this earlier work has been integrated and reproduced in this paper. The relevant figures (Figures 2 to 4) and tables (Tables 5 to 8) are explicitly referenced where the figures or the tables are discussed.…”

mentioning

confidence: 99%

“…Pattern-making for clothing with a good fit requires the characteristic features of a body to be known, and the body features and curve parameters in a customized pattern to be mutually adaptive. 11,[13][14][15][16] The level of individualization of the pattern depends on the fitting level of curves with their corresponding features. Originating from this concept, our study intends to develop a method that combines the advantages of the two pattern-alteration categories discussed above for the development of personalized patterns.…”

mentioning

confidence: 99%

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Converting lower-body features from three-dimensional body images into rules for individualized pant patterns

Jian-jun

Kong

Kuang

et al. 2018

Textile Research Journal

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This paper focuses on a new method to classify and convert the lower-body features of young females into the rules of individualized elements that are necessary for pant pattern design. Approaches used to develop a customized apparel pattern and their advantages and disadvantages are discussed in the literature review. Subsequently, a new method to analyze lower-body features for the development of individualized pant patterns is presented. The main method involved is the classification of certain body features into grades that provided alternatives for different body shapes, and the use of elements that are needed for pattern-making but that are difficult to measure directly in a three-dimensional image to predict the girths, half-girths and the other key measurements of a body. Three sets of rules were derived from this method: (a) individualized rules on height to classify the height at a feature into different grades, which help to find the location of the feature based on the height of a body; (b) individualized rules on girth to sort the thickness/width ratio of different cross-sections into grades at different features, which predict the girth of a characteristic feature from the thickness and the width of a feature; and (c) individualized rules on the crotch to find the relationship between the length of the measuring line at the divided points and the thickness of a certain girth, which reveal the individuality of the crotch and benefit the development of an individualized pant pattern. The method presented has the potential to realize individuality in female pant design by converting lower-body features into grades and establishing relationships with personalized parameters. This method can reduce the complexity of customization in pattern design.

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Section: Methodsmentioning

confidence: 94%

Section: Individualized Rules On Girthmentioning

confidence: 92%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Converting lower-body features from three-dimensional body images into rules for individualized pant patterns

Jian-jun

Kong

Kuang

et al. 2018

Textile Research Journal

Self Cite

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“…In doing so, garment development practitioners, industrial ergonomists, and scientists perceive Body Scanning as being a precise measurement tool (Kuehnapfel et al 2016;Danckaers et al 2019) to uncover content validity or 'ground truth'. Faith in 3 D Body Scanning's precision leads many garment manufacturers to attempt Body Scanning's application in their product development process (Su et al 2017;D'Apuzzo 2007;Chan, Fan, and Yu 2005;Lu, Song, and Li 2014). However, 3 D Body Scanning, like manual measurement, has limited context in garment development practice and inherent measurement errors.…”

Section: Introductionmentioning

confidence: 99%

A method for increasing 3D body scanning’s precision: Gryphon and consecutive scanning

et al. 2021

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The fashion industry cannot use 3 D Body Scanning to create custom garment patterns because its measurements fail to meet ISO 20685:2010's tolerances. To advance 3 D Body Scanning's precision, we present Gryphon: an algorithm that removes the two most extreme measurements from five body scans; removing potentially erroneous data. We assess Gryphon's precision against current industry practice, determine if consecutive and non-consecutive data capture influences precision, and determine 3 D Body Scanning's inherent imprecision inherent. We analyse 97 participants over 121 industry-standard measurements for consecutive and nonconsecutive data-capture through MANOVA statistical analysis. Under current industry practice, only one measurement meets ISO 20685. However, under Gryphon and consecutive scanning, 97.5% of measurements meet ISO 20685. We also prove that the body's in-scan movement does not affect reliability. Ultimately, we offer the fashion industry, ergonomists, and practitioners an accessible method to increase 3 D Body Scanning's precision at a level unavailable under previous methods.

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An intelligent recommendation system for personalised parametric garment patterns by integrating designer’s knowledge and 3D body measurements

Chi,

Zeng,

Bruniaux

et al. 2024

Ergonomics

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A Method to Develop a Personalized Pattern of Pant from 3D Scanning Data

Cited by 3 publications

References 13 publications

Converting lower-body features from three-dimensional body images into rules for individualized pant patterns

Converting lower-body features from three-dimensional body images into rules for individualized pant patterns

A method for increasing 3D body scanning’s precision: Gryphon and consecutive scanning

An intelligent recommendation system for personalised parametric garment patterns by integrating designer’s knowledge and 3D body measurements

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