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Modeling of female human body shapes for apparel design based on cross mean sets

机译:基于交叉均值集的服装设计女性人体形态建模

摘要

This paper is concerned with a method to build prototypes of human bodies that can be used for apparel design. One of the most important issues in the apparel development process is to define a sizing system to provide a good fitting for the majority of the population. Since anthropometric measures do not present the same linear growth with size in each dimension, it is very important to find a prototype that represents as accurately as possible each class in the sizing system. In this paper we propose a method based on the concept of random compact mean set to define prototypes in apparel design. From a cloud of 3D points obtained with a 3D scanner a solid that represents the human body is obtained. 2D cross sections of this solid are extracted at certain heights corresponding to key points of the body. These different cross-sections can be seen as a realization of a random compact set in the plane. A very popular definition on mean set is applied to each sample of 2D cross sections, and finally the prototype is obtained as the 3D reconstruction of these 2D mean sections. As a real example, the proposed methodology is applied to the 3D database obtained from a anthropometric survey of the Spanish female population conducted in this country in 2006
机译:本文涉及一种可用于服装设计的人体原型的构建方法。服装开发过程中最重要的问题之一是定义一个尺寸调整系统,以使其适合大多数人。由于人体测量学在每个维度上的大小并没有呈现出相同的线性增长,因此找到一个能够尽可能准确地表示尺寸系统中每个类别的原型非常重要。在本文中,我们提出了一种基于随机紧凑均值集概念的方法来定义服装设计中的原型。从使用3D扫描仪获得的3D点云中,获得代表人体的实体。在对应于主体关键点的某些高度提取此实体的2D横截面。这些不同的横截面可以看作是平面中随机紧凑集的实现。在均值集上非常流行的定义适用于2D横截面的每个样本,最后获得原型作为这些2D均值截面的3D重建。举一个真实的例子,该提议的方法应用于从3D数据库中获得的3D数据库,该数据库是2006年在该国对西班牙女性人口进行的人体测量研究得出的

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