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Abstract

The construction of 3D (three-dimensional) human model is the key link to the development of intelligent technology in garment engineering. In order to establish more convenient and efficient 3D human reconstruction methods based on semantic-driven parameters, we study semantic parameter prediction and 3D human reconstruction. Based on the self-collection database, we adopt the semantic parameter prediction method combining strong and weak correlations, and innovatively realize the automatic prediction of the remaining 12 parameters by entering the two parameters of height and weight. Then, the mapping relationship between the 14 semantic parameters and the 3D human template model is established to complete the personalized 3D human body reconstruction. Results show that the reconstruction error of each part of personalized 3D human body reconstruction is basically within ±1.5 cm, which meets the requirements of 3D human modeling for clothing size design. Further studies can use these results for the development and intellectualization of digital garment engineering

Keywords

Garment engineering semantic-driven parameter prediction personalization human reconstruction

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References

Zhou

Long

, et al. Computational design of shape-changing robotic mannequin based on 3D human models. Text Res J 2021; 91: 1065–1082.

Wang

Tian

BQ.

Research progress of personalized 3D human modeling in virtual fitting. Text Herald 2020; 39(07): 79–82.

Chi

Zeng

Bruniaux

, et al. A new parametric 3D human body modeling approach by using key position labeling and body parts segmentation. Text Res J 2022; 92: 3653–3679.

, et al. Design of a multi-sensor information acquisition system for mannequin reconstruction and human body size measurement under clothes. Text Res J 2022; 92: 3750–3765.

Wang

HB.

Finite element modelling of Chinese male office workers' necks using 3D body measurements. J Text I 2017; 108: 766–775.

Liu

Huang

, et al. Template deformation-based 3D reconstruction of full human body scans from low-cost depth cameras. IEEE T Cybernetics 2017; 47: 695–708.

Yao

Peng

Guo

, et al. A data-driven approach for 3D human body pose reconstruction from a kinect sensor. J Phys Conf Ser 2018; 1098(1): 354–371.

Huang

Kwok

Zhou

Parametric design for human body modeling by wireframe-assisted deep learning. Comput Aid Des 2019; 108: 19–29.

, et al. Measuring and modeling human bodies with a novel relocatable mechatronic sensor-net. Text Res J 2019; 89: 4131–4147.

10.

Xie

Zhong

, et al. Non-parametric anthropometric graph convolutional network for virtual mannequin reconstruction. IEEE Acc 2020; 8: 3539–3550.

11.

GB/T 30548:2014. Verification method of anthropometric data in clothing field-obtained by using 3D measurement instrument.

12.

Pei

Park

Ashdown

SP.

Female breast shape categorization based on analysis of CAESAR 3D body scan data. Text Res J 2019; 89: 590–611.

13.

Pishchulin

Wuhrer

Helten

, et al. Building statistical shape spaces for 3D human modeling. Pattern Recogn 2017; 67: 276–286.

14.

Wang

Zhang

, et al. PanoMan: sparse localized components–based model for full human motions. TOG 2021; 42: 19–36.

15.

Allen

Curless

Popovic

The space of human body shapes: reconstruction and parameterization from range scans. TOG 2003; 22: 587–594.

16.

GB/T 16160:2017. Anthropometric definitions and methods for garment.

17.

Xie

Zhong

YQ.

Structure-consistent customized virtual mannequin reconstruction from 3D scans based on optimization. Text Res J 2020; 90: 937–950.

18.

Zhang

Liu

XL.

Regression analysis of women's leg size. J Silk China, 2017; 54: 31–35.

19.

Gui

Research on key technology of human body model matching based on 3D measurement. Dissertation, Dalian Polytechnic University, 2020.

20.

Zhu

Wang

Fuzzy monotonic K-nearest neighbor versus monotonic fuzzy K-nearest neighbor. IEEE Trans Fuzzy Syst 2022; 30: 3501–3513.

21.

Cheng

Chen

Martin

, et al. Parametric modeling of 3D human body shape-a survey. Comput Graph 2017; 71: 88–100.

22.

Wang

, et al. Shape-from-mask: a deep learning based human body shape reconstruction from binary mask images. Graph 2018; 1: 1–11.

23.

Zhou

Mao

Zhang

, et al. Selection of breast features for young women in northwestern China based on the random forest algorithm. Text Res J 2022; 92: 957–973.

24.

Zhu

Duan

Animal fiber imagery classification using a combination of random forest and deep learning methods. J Eng Fibers Fabr 2021; 16: 1–8.

25.

Zhong

ZJ.

Girth fitting of young women based on body digital images. J Silk China 2019; 56: 46–51.

26.

Wang

QL.

Parametric human body modeling and its rapid body measurement technology application. Shanghai: Shanghai University of Engineering Science, 2021.

27.

Kuang

Jiang

, et al. Young male's girth fitting of remote measurement based on HSI color model. Knit Ind 2023; 51(07): 70–75.

The construction of a three-dimensional human body based on semantic-driven parameters to improve virtual fitting

Abstract

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