Classification of upper lateral body shapes for the apparel industry

The lateral body shape is a critical determiner of the fit of garments. Either visual assessment or statistical analysis methods have been used to classify the lateral body types. These methods are limited to some extent since various anthropometric features inherently coexist and interact in a human body shape. This study aims to develop objective criteria for the classification of upper lateral body shapes integrating visual assessment and statistical analysis. The three‐dimensional scan data of 246 women between 18 and 49 years old were visually classified into four lateral body shapes by an expert panel. In addition, the back space and lateral angles extracted from the scan data were employed for further statistical analyses. Multinomial logistic regressions were used to develop logit models for lateral body types. It was concluded that the resulting logit models could classify lateral body types and calculate the probability of a set of body scan data being classified as a certain lateral body type. It is expected that this probability might be a guideline to quantify the characteristics of the lateral body shape in the apparel industry. © 2010 Wiley Periodicals, Inc.     

An XML-based methodology for parametric temporal database model implementation

Parametric data model is one of dimensional data models. It defines attributes as functions, modeling a real world object into a single tuple in a database. Such one-to-one correspondence between an object in the real world and a tuple provides various advantages in modeling dimensional data, avoiding self-joins which frequently appear in temporal data models which fragment an object into multiple tuples. Despite its modeling advantages, it is impractical to implement the parametric data model on top of conventional database systems because of the data model’s variable attribute sizes. However, such implementation challenge can be resolved by XML because XML is flexible for data boundaries. In this paper, we present an XML-based implementation methodology for the parametric temporal data model. In our implementation, we develop our own XML storage called CanStoreX (Canonical Storage for XML) and build the temporal database system on top of the CanStoreX.     

An efficient human model customization method based on orthogonal-view monocular photos

Human body modeling is a central task in computer graphics. In this paper, we propose an intelligent model customization method, in which customer’s detailed geometric characteristics can be reconstructed using limited size features extracted from the customer’s orthogonal-view photos. To realize model customization, we first propose a comprehensive shape representation to describe the geometrical shape characteristics of a human body. The shape representation has a layered structure and corresponds to important feature curves that define clothing size. Next, we identify and model a novel relationship model between 2D size features and 3D shape features for each cross-section using real subject scanned data. We predict a customer’s cross-sectional 3D shape based on size features extracted from the customer’s photos, and then we reconstruct the customer’s shape representation using predicted cross-sections. We develop a new deformation algorithm that deforms a template model into a customized shape using the reconstructed 3D shape representation. A total of 30 subjects, male and female, with varied body shapes have been recruited to verify the model customization method. The customized models show high degree of resemblance of the subjects, with accurate body sizes; the accuracy of the models is comparable to scan. It shows that the method is a feasible and efficient solution for human model customization that fulfills the specific needs of the clothing industry.     

Parametric body shape model of standing children aged 3–11 years

A statistical body shape model (SBSM) for children was developed for generating a child body shape with desired anthropometric parameters. A standardised template mesh was fit to whole-body laser scan data from 137 children aged 3–11 years. The mesh coordinates along with a set of surface landmarks and 27 manually measured anthropometric variables were analysed using principal component (PC) analysis. PC scores were associated with anthropometric predictors such as stature, body mass index (BMI) and ratio of erect sitting height to stature (SHS) using a regression model. When the original scan data were compared with the predictions of the SBSM using each subject's stature, BMI and SHS, the mean absolute error was 10.4 ± 5.8 mm, and 95th percentile error was 24.0 ± 18.5 mm. The model, publicly available online, will have utility for a wide range of applications.

Practitioner Summary: A statistical body shape model for children helps to account for inter-individual variability in body shapes as well as anthropometric dimensions. This parametric modelling approach is useful for reliable prediction of the body shape of a specific child with a few given predictors such as stature, body mass index and age.     

基于尺寸建立个性化人体模型的方法

近年来,人体模型定制已成为计算机图形学领域的重要研究课题之一。文中提出一种基于少量人体尺寸生成个性化人体模型的方法——分块优化法。首先,根据MPI人体扫描模型数据库获得人体外形形变参数与尺寸参数,采用线性相关分析方法实现由若干尺寸恢复完整人体尺寸集。其次,通过参数优化的线性回归方法分析各部位三维人体外形参数与二维尺寸数据之间的关系,并根据输入尺寸对人体模型进行进一步精调。实验表明,上述方法能够生成准确反映人体外形的人体模型。     

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types.Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.     

Estimating body shape of dressed humans

The paper presents a method to estimate the detailed 3D body shape of a person even if heavy or loose clothing is worn. The approach is based on a space of human shapes, learned from a large database of registered body scans. Together with this database we use as input a 3D scan or model of the person wearing clothes and apply a fitting method, based on ICP (iterated closest point) registration and Laplacian mesh deformation. The statistical model of human body shapes enforces that the model stays within the space of human shapes. The method therefore allows us to compute the most likely shape and pose of the subject, even if it is heavily occluded or body parts are not visible. Several experiments demonstrate the applicability and accuracy of our approach to recover occluded or missing body parts from 3D laser scans.     

Child body shape measurement using depth cameras and a statistical body shape model

We present a new method for rapidly measuring child body shapes from noisy, incomplete data captured from low-cost depth cameras. This method fits the data using a statistical body shape model (SBSM) to find a complete avatar in the realistic body shape space. The method also predicts a set of standard anthropometric data for a specific subject without measuring dimensions directly from the fitted model. Since the SBSM was developed using principal component (PC) analysis, we formulate an optimisation problem to fit the model in which the degrees of freedom are defined in PC-score space. The mean unsigned distance between the fitted-model based on depth-camera data and the high-resolution laser scan data was 9.4 mm with a standard deviation (SD) of 5.1 mm. For the torso, the mean distance was 2.9 mm (SD 1.4 mm). The correlations between standard anthropometric dimensions predicted by the SBSM and manually measured dimensions exceeded 0.9.     

A Computational Model of Multidimensional Shape

We develop a computational model of shape that extends existing Riemannian models of curves to multidimensional objects of general topological type. We construct shape spaces equipped with geodesic metrics that measure how costly it is to interpolate two shapes through elastic deformations. The model employs a representation of shape based on the discrete exterior derivative of parametrizations over a finite simplicial complex. We develop algorithms to calculate geodesics and geodesic distances, as well as tools to quantify local shape similarities and contrasts, thus obtaining a formulation that accounts for regional differences and integrates them into a global measure of dissimilarity. The Riemannian shape spaces provide a common framework to treat numerous problems such as the statistical modeling of shapes, the comparison of shapes associated with different individuals or groups, and modeling and simulation of shape dynamics. We give multiple examples of geodesic interpolations and illustrations of the use of the models in brain mapping, particularly, the analysis of anatomical variation based on neuroimaging data.     

Data mining in forecasting PVT correlations of crude oil systems based on Type1 fuzzy logic inference systems

Pressure–volume–temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson–Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.     

A topology-preserving optimization algorithm for polycube mapping

We present an effective optimization framework to compute polycube mapping. Composed of a set of small cubes, a polycube well approximates the geometry of the free-form model yet possesses great regularity; therefore, it can serve as a nice parametric domain for free-form shape modeling and analysis. Generally, the more cubes are used to construct the polycube, the better the shape can be approximated and parameterized with less distortion. However, corner points of a polycube domain are singularities of this parametric representation, so a polycube domain having too many corners is undesirable. We develop an iterative algorithm to seek for the optimal polycube domain and mapping, with the constraint on using a restricted number of cubes (therefore restricted number of corner points). We also use our polycube mapping framework to compute an optimal common polycube domain for multiple objects simultaneously for lowly distorted consistent parameterization.     

基于二维点云图的三维人体建模方法

近年来基于二维图像的三维建模方法取得了快速发展,但就人体建模而言,由于摄像头采集到的二维人体图像包含衣物、发丝等大量的纹理信息,而像虚拟试衣等相关应用需要将人体表面的衣物褶皱等纹理信息去除,同时考虑到裸体数据采集侵犯了用户的隐私,因此提出一种基于二维点云图像到三维人体模型的新型建模方法。与摄像机等辅助设备进行二维图片数据集的采集不同,该算法的输入是由三维人体点云模型以顶点模式绘制的二维点云渲染图。主要工作是建立一个由二维点云图和相应的人体黑白二值图构成的数据集,并训练一个由前者生成后者的生成对抗网络模型。该模型将二维点云图转化为相应的黑白二值图。将该二值图输入一个训练好的卷积神经网络,用于评估二维图像到三维人体模型构建的效果。考虑到由不完整三维点云数据重建完整的三维人体网格模型是一个具有挑战性的问题,因此通过模拟二维点云的破损和残缺状态,使得算法能够处理不完整的二维点云图。大量的实验结果表明,该方法重建出的三维人体模型能够有效实现视觉上的真实感,为了对重建后的精度进行定量的分析,选取了人体特征中具有代表性的腰围特征作为误差评估;为了增加三维人体模型库中人体形态的多样性,还引入一种便捷的三维人体模型数据增强技术。实验结果表明,该算法只需要输入一张二维点云图像,就能快速创建出相应的数字化人体模型。     

Real-time speech-driven face animation with expressions using neural networks

A real-time speech-driven synthetic talking face provides an effective multimodal communication interface in distributed collaboration environments. Nonverbal gestures such as facial expressions are important to human communication and should be considered by speech-driven face animation systems. In this paper, we present a framework that systematically addresses facial deformation modeling, automatic facial motion analysis, and real-time speech-driven face animation with expression using neural networks. Based on this framework, we learn a quantitative visual representation of the facial deformations, called the motion units (MUs). A facial deformation can be approximated by a linear combination of the MUs weighted by MU parameters (MUPs). We develop an MU-based facial motion tracking algorithm which is used to collect an audio-visual training database. Then, we construct a real-time audio-to-MUP mapping by training a set of neural networks using the collected audio-visual training database. The quantitative evaluation of the mapping shows the effectiveness of the proposed approach. Using the proposed method, we develop the functionality of real-time speech-driven face animation with expressions for the iFACE system. Experimental results show that the synthetic expressive talking face of the iFACE system is comparable with a real face in terms of the effectiveness of their influences on bimodal human emotion perception.     

Bio-surfaces and geometric references for mass customization in bio-interface design

Mass customization of products that interface with the human body poses unique problems due to the complexities of bio-interface design, the lack of biomechanical techniques in traditional mechanical design, and the absence of specific parametric strategies. Current biomechanical design often follows craftsman-like design processes using less than state-of-the-art tools and techniques. Thus, products that interface with the human body are not readily parameterized or automated. This paper presents a strategy for implementing mass customization in the design of mechanical devices that interface with the human body. This strategy is based on three methods that include: a method for capturing and representing the human body so that the model can be used with state-of-the-art tools and solid modeling techniques, a design methodology based on feature structure planning allowing the design process to be reused and automated, and a strategy for identifying parametric variables tied to the human body. A case study is presented to illustrate the proposed process.     

Characterization of neuropathological shape deformations

We present a framework for analyzing the shape deformation of structures within the human brain. A mathematical model is developed describing the deformation of any brain structure whose shape is affected by both gross and detailed physical processes. Using our technique, the total shape deformation is decomposed into analytic modes of variation obtained from finite element modeling, and statistical modes of variation obtained from sample data. Our method is general, and can be applied to many problems where the goal is to separate out important from unimportant shape variation across a class of objects. In this paper, we focus on the analysis of diseases that affect the shape of brain structures. Because the shape of these structures is affected not only by pathology but also by overall brain shape, disease discrimination is difficult. By modeling the brain's elastic properties, we are able to compensate for some of the nonpathological modes of shape variation. This allows us to experimentally characterize modes of variation that are indicative of disease processes. We apply our technique to magnetic resonance images of the brains of individuals with schizophrenia, Alzheimer's disease, and normal-pressure hydrocephalus, as well as to healthy volunteers. Classification results are presented     

Nonlinear regression equations for segmental mass-inertial characteristics of Korean adults estimated using three-dimensional range scan data

Human modeling and the biomechanical analysis of human movement require the accurate estimation of body segment parameters for various populations and individuals. In this study, the body characteristics of adult Koreans were investigated using three-dimensional range scan data for 40 males and 40 females aged between 18 and 59 years obtained from the SizeKorea anthropometric database. Each subject was divided into 16 segments, and the mass-inertial parameters of each segment were calculated under the assumption of a uniform density distribution for the segment. The length and at least one circumference of each segment were determined from the scan data for that segment. Nonlinear regression equations were then derived based on the segment lengths and circumferences. The body segment parameters of Korean adults can be estimated using these equations if the required dimensions are measured directly or derived from an anthropometric database.     

The 3D Chinese head and face modeling

Perfect fit for people has always been a target for product design. Designers commonly use traditional anthropometric dimensions for 3D product design thus creating a lot of fitting problems when dealing with the complexities of human body shapes. The development of recent 3D anthropometric survey has created an opportunity for complex shape analysis on human model by collecting 3D scan data. Using 3D point cloud data from the SizeChina survey, a methodology of creating a homologous 3D head and face model was demonstrated in this study. Anatomical and virtual landmarks, and surface modeling algorithm based on point cloud data were applied in building the model. The head and face models for all scans had the same amount of vertices with consistent features. The average Chinese models showed obvious differences between male and female. The variations of head and face shapes were analyzed using Principal Component Analysis and the results showed that the largest variations among people were general size, especially for width and depth. However face height, forehead, back of the head, chin and jaw area were also important when describing the 3D shape. The results from this study may be useful in the design of head and facial products.     

基于维度建模的MDSS多维模型设计

雏度建模是一种有效的建立数据仓库应用的建模方法.论述了维度建模的一般方法,并结合国家气象中心MDSS模型的设计,分析、讨论了维度建模中的一些关键技术问题,特别提出在构建物理模型的同时定义统计分析的计算方法,从而降低数据准备的复杂性,提高数据仓库建设的质量和访问效率.