Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (10): 146-149.doi: 10.13475/j.fzxb.20200802605

• Apparel Engineering • Previous Articles     Next Articles

Expression and realization of human body model based on learning model

JI Yong1,2, JIANG Gaoming1,3()   

  1. 1. Engineering Research Center for Knitting Technology, Ministry of Education, Jiangnan University, Wuxi, Jiangsu 214122, China
    2. Xinlin College, Nantong University, Natong, Jiangsu 226001, China
    3. Key Laboratory of Eco-Textiles(Jiangnan University), Ministry of Education, Wuxi, Jiangsu 214122, China
  • Received:2020-08-14 Revised:2021-06-30 Online:2021-10-15 Published:2021-10-29
  • Contact: JIANG Gaoming E-mail:jgm@jiangnan.edu.cn

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Abstract:

In order to solve the problem of human body reconstruction with complex structure, this paper proposes a representation method of human body model based on learning model. The linear model of human body adopts the mesh vertex algorithm to correct the shape of human body. According to the standard method of creating human body mesh, the shape of human body model is formed by the average template represented by vector. By learning the regression matrix of different human body shapes, the linear model of human body is segmented and the depth is estimated. The spatial relationship of human body model is implicitly established, and the joint position in the linear model of human body is estimated, so that the trunk expression of human body model is more natural and clear. The results show that the human model expression algorithm based on learning model is reliable, and the output of the model is more accurate. The model can achieve efficient human model reconstruction and optimization, and provide technical basis and theoretical reference for the effective expression of human model.

Key words: learning model, human body model, three-dimensional human body, grid, network structure

CLC Number: 

  • TS941

Fig.1

Passive triangulation"

Fig.2

Active triangulation"

Fig.3

Mannequin grid datum"

Tab.1

Mannequin segmentation"

试验方法 交并比
均值		 优化Obj
文件效率/%		 精度
均值		 时间/s
Optimal 49.56 100 47.61 103.2
Baseline-greedy 35.42 69 36.48 60.5
本文方法 65.27 83 62.15 69.1

Fig.4

Linear mannequin"

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