A new parametric 3D human body modeling approach by using key position labeling and body parts segmentation. Issue 19 (October 2022)

• Record Type: Journal Article
• Authors: Chi, Cheng; Zeng, Xianyi; Bruniaux, Pascal; Tartare, Guillaume; Jin, Hongshu
• Is Part Of: Textile research journal. Volume 92:Issue 19/20(2022)
• Page Start: 3653
• Page End: 3679
• Publication Date: 2022-10
• Subjects: 3D human body model -- designer's knowledge -- key body position labeling -- body part segmentation; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/00405175221089688
• Languages: English
• ISSNs: 0040-5175

Abstract

The current three-dimensional human models used in the apparel industry are mostly rigid and lack semantic information on body positions and body parts. Therefore, it is difficult for designers to make accurate, fast and effective designs from these models. This paper proposes a new parametric three-dimensional human body model based on key position labeling and optimized body parts segmentation. First, by using experts' professional knowledge, we manually realize accurate human body data measurements as well as their interpretation and classification, and extract relevant human body features. After deep analysis, measured data irrelevant to body shape have been excluded by designers. Furthermore, the relation between body shapes and body features have been modeled. Second, based on this relational model, we label key positions on the corresponding three-dimensional body model obtained by scanning and segmenting the whole three-dimensional human body into semantically interpretable body parts. In this way, two databases have been created, enabling us to identify features of all segmented body parts, whose combination corresponds to the whole body shape. Third, for a specific consumer, his/her personalized three-dimensional human model can be obtained by taking a very few number of body measurements on himself/herself, making an appropriate combination of the identified body parts, and adjusting parameters of all involved body parts. By comparing the proposed labeled and segmented three-dimensional human model and the existing human models through a number of experiments, the proposed model leads to more relevant results with high accuracy and high visual quality related to real human body shapes.