Volume 4, Issue 2
Three Dimensional Pattern Grading Based on Deformable Body Features and 3D Developable Surface

Yingnan Wang & Haiqiao Huang

Journal of Fiber Bioengineering & Informatics, 4 (2011), pp. 115-128.

Published online: 2011-04

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  • Abstract

The garment pattern grading is a crucial procedure for manufacturing full sizes of clothing products. This procedure traditionally starts with an ‘average’ pattern and generates a set of sized garment patterns by extending the ‘average’ pattern. The quality of the graded patterns depends on the grading technique and the technician's experience. However, the garment patterns are often graded based on two-dimensional rules that hardly provide an accurate fit because of shape variations and complexity of 3D human bodies. Additionally, the traditional pattern grading is conducted manually and very time-consuming. In this paper, we propose a new automatic approach of generating full sizes of garment patterns by flattening 3D garments created from parameterized mannequins in fulfilling the requirements of body structures, sizing chart and garment fit. Firstly, a parametric human body model is introduced called Horizontal Piecewise B-spline Curves (HPBC) model. Three types of novel frames are developed from the HPBC model, namely feature frame, size frames, and ease frames. Based on these frames, a range of fit and flattenable 3D garments are established. Finally, the graded patterns can be generated by flattening these 3D garments.

  • Keywords

3D Pattern Grading Garment Grading Developable Garment Ease Distribution Sizing Chart

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@Article{JFBI-4-115, author = {}, title = {Three Dimensional Pattern Grading Based on Deformable Body Features and 3D Developable Surface}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2011}, volume = {4}, number = {2}, pages = {115--128}, abstract = {The garment pattern grading is a crucial procedure for manufacturing full sizes of clothing products. This procedure traditionally starts with an ‘average’ pattern and generates a set of sized garment patterns by extending the ‘average’ pattern. The quality of the graded patterns depends on the grading technique and the technician's experience. However, the garment patterns are often graded based on two-dimensional rules that hardly provide an accurate fit because of shape variations and complexity of 3D human bodies. Additionally, the traditional pattern grading is conducted manually and very time-consuming. In this paper, we propose a new automatic approach of generating full sizes of garment patterns by flattening 3D garments created from parameterized mannequins in fulfilling the requirements of body structures, sizing chart and garment fit. Firstly, a parametric human body model is introduced called Horizontal Piecewise B-spline Curves (HPBC) model. Three types of novel frames are developed from the HPBC model, namely feature frame, size frames, and ease frames. Based on these frames, a range of fit and flattenable 3D garments are established. Finally, the graded patterns can be generated by flattening these 3D garments.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbi06201102}, url = {http://global-sci.org/intro/article_detail/jfbi/4908.html} }
TY - JOUR T1 - Three Dimensional Pattern Grading Based on Deformable Body Features and 3D Developable Surface JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 115 EP - 128 PY - 2011 DA - 2011/04 SN - 4 DO - http://dor.org/10.3993/jfbi06201102 UR - https://global-sci.org/intro/jfbi/4908.html KW - 3D Pattern Grading KW - Garment Grading KW - Developable Garment KW - Ease Distribution KW - Sizing Chart AB - The garment pattern grading is a crucial procedure for manufacturing full sizes of clothing products. This procedure traditionally starts with an ‘average’ pattern and generates a set of sized garment patterns by extending the ‘average’ pattern. The quality of the graded patterns depends on the grading technique and the technician's experience. However, the garment patterns are often graded based on two-dimensional rules that hardly provide an accurate fit because of shape variations and complexity of 3D human bodies. Additionally, the traditional pattern grading is conducted manually and very time-consuming. In this paper, we propose a new automatic approach of generating full sizes of garment patterns by flattening 3D garments created from parameterized mannequins in fulfilling the requirements of body structures, sizing chart and garment fit. Firstly, a parametric human body model is introduced called Horizontal Piecewise B-spline Curves (HPBC) model. Three types of novel frames are developed from the HPBC model, namely feature frame, size frames, and ease frames. Based on these frames, a range of fit and flattenable 3D garments are established. Finally, the graded patterns can be generated by flattening these 3D garments.
Yingnan Wang & Haiqiao Huang. (2019). Three Dimensional Pattern Grading Based on Deformable Body Features and 3D Developable Surface. Journal of Fiber Bioengineering and Informatics. 4 (2). 115-128. doi:10.3993/jfbi06201102
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