Parametric human body shape modeling framework for human-centered product design

The objective of this study is the development of a novel parametric human body shape modeling framework for integration into various product design applications. Our modeling framework is comprised of three phases of database construction, statistical analysis, and model generation. During the database construction phase, a 3D whole body scan data of 250 subjects are obtained, and their data structures are processed so as to be suitable for statistical analysis. Using those preprocessed scan data, the characteristics of the human body shape variation and their correlations with several items of body sizes are investigated in the statistical analysis phase. The correlations obtained from such analysis allow us to develop an interactive modeling interface, which takes the body sizes as inputs and returns a corresponding body shape model as an output. Using this interface, we develop a parametric human body shape modeling system and generate body shape models based on the input body sizes. In our experiment, our modeler produced reasonable results having not only a high level of accuracy but also fine visual fidelity. Compared to other parametric human modeling approaches, our method contributes to the related field by introducing a novel method for correlating body shape and body sizes and by establishing an improved parameter optimization technique for the model generation process.     

Body shape analyses of large persons in South Korea

Despite the prevalence of obesity and overweight, anthropometric characteristics of large individuals have not been extensively studied. This study investigated body shapes of large persons (Broca index ≥ 20, BMI ≥ 25 or WHR>1.0) using stature-normalised body dimensions data from the latest South Korean anthropometric survey. For each sex, a factor analysis was performed on the anthropometric data set to identify the key factors that explain the shape variability; and then, a cluster analysis was conducted on the factor scores data to determine a set of representative body types. The body types were labelled in terms of their distinct shape characteristics and their relative frequencies were computed for each of the four age groups considered: the 10s, 20s–30s, 40s–50s and 60s. The study findings may facilitate creating artefacts that anthropometrically accommodate large individuals, developing digital human models of large persons and designing future ergonomics studies on largeness.

Practitioner Summary: This study investigated body shapes of large persons using anthropometric data from South Korea. For each sex, multivariate statistical analyses were conducted to identify the key factors of the body shape variability and determine the representative body types. The study findings may facilitate designing artefacts that anthropometrically accommodate large persons.     

Fuzzy classification of young women's lower body based on anthropometric measurement

The traditional method of body classification is discrete, using crisp and rather dichotomous classification methods; there are many shortcomings for ergonomic design of clothing products by this method. This paper proposes a fuzzy method to classify lower body shapes based on triangular fuzzy numbers. By using factor analysis and correlation analysis, we found that the height, the waist girth, and the difference of hip-waist are crucial dimensions to represent lower body shape. We then classified the lower body shape into three categories according to the difference of hip-to-waist, and finally used the membership of triangular fuzzy numbers to represent the lower body shapes. Results show that the fuzzy method of body classification can more accurately represents body information than the traditional method without increasing the number of body types. Additionally, we established that the mean of the height, waist girth and hip girth of the young women of northeast China increased by about 0.8 cm, 1.5 cm and 1.4 cm respectively compared with ten years ago.Relevance to industry: Anthropometric data is the basis of garment pattern design, and body classification is a necessary precondition for developing a garment size system. These research achievements will add value to the pattern design of young women's lower body clothing, the development of new sizing systems and related industries.     

Automatic body landmark identification for various body figures

In this paper, we presented automatic body landmark identification algorithms that deals flexibly with the difference in body shapes and reduces the inconsistency resulting from the differences in body shapes. First, the landmark search range was defined using the statistical analysis. Next, body scan direction was identified and it was segmented. Next, automatic landmark identification algorithms were developed for each of the six landmarks and the accuracy was examined for each body shape. The scans were extracted from 5th Size Korea database. This algorithms were successfully tested on various body shapes and improved the robustness.

Relevance to industry

In automatic body measurement systems, the landmark location error occurring at nonstandard body shapes nullifies the advantage of saving time. It also makes the 3D scan measurements unreliable. The improvement of reliability and accuracy of the automatic 3D body measurement algorithm for various human body shapes will reduce the time for performing measurements and be practical for use in human-size-related production processes.     

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.     

Estimating 3D human shapes from measurements

Recent advances in 3D imaging technologies give rise to databases of human shapes, from which statistical shape models can be built. These statistical models represent prior knowledge of the human shape and enable us to solve shape reconstruction problems from partial information. Generating human shape from traditional anthropometric measurements is such a problem, since these 1D measurements encode 3D shape information. Combined with a statistical shape model, these easy-to-obtain measurements can be leveraged to create 3D human shapes. However, existing methods limit the creation of the shapes to the space spanned by the database and thus require a large amount of training data. In this paper, we introduce a technique that extrapolates the statistically inferred shape to fit the measurement data using non-linear optimization. This method ensures that the generated shape is both human-like and satisfies the measurement conditions. We demonstrate the effectiveness of the method and compare it to existing approaches through extensive experiments, using both synthetic data and real human measurements.     

Automatic modeling of virtual humans and body clothing

Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. The problem and solutions to automatic modeling of animatable virtual humans are studied. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewed.     

Detection and classification of topological evolution for linear metamorphosis

The advantage of functional methods for shape metamorphosis is the automatic generation of intermediate shapes possible between the key shapes of different topology types. However, functional methods have a serious problem: shape interpolation is applied without topological information and thereby the time values of topological changes are not known. Thus, it is difficult to identify the time intervals for key frames of shape metamorphosis animation that faithfully visualize the topological evolution. Moreover, information on the types of topological changes is missing. To overcome the problem, we apply topological analysis to functional linear shape metamorphosis and classify the type of topological evolution by using a Hessian matrix. Our method is based on Morse theory and analyzes how the critical points appear. We classify the detected critical points into maximum point, minimum point, and saddle point types. Using the types of critical points, we can define the topological information for shape metamorphosis. We illustrate these methods using shape metamorphosis in 2D and 3D spaces.     

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.     

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.     

Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation

This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data.     

Shape recognition by a scale-invariant model

In this article, we considered the recognition of unknown shapes by maximum likelihood methods. The contour of a shape is represented by its centroidal profile, and it is fitted by a circular autoregressive model. Two different shape recognition problems are considered: the decision on the similarity of two unknown shapes, and the classification of an unknown shape as one of many known shapes. Maximum likelihood decision rules for these two cases are derived. The decision rules are invariant to translation, rotation, and size change after normalizing the estimates. The developed algorithms are applied to classify eight classes of machine parts and eight classes of aircraft shapes. For each class, 60 to 80 samples are generated by rotating and dilating the original shape. In the experiment, more than 98% of machine parts are classified correctly, and more than 97% of aircraft shapes are correctly classified. This result is better than previous model-based approaches. Partially supported by the National Science Foundation under the grant IRI-8809391.     

3D standard avatar creation of Korean women in their twenties and thirties by body types for apparel industry

The purposes of this study are to classify body types of Korean women in their twenties and thirties for the creation of the 3D avatars and to propose the representative body size of each body type by analyzing the body size of Korean women in their twenties and thirties, to propose a 3D avatar modeling process design that reflects the body shapes of Korean women in their twenties and thirties, and to present standard 3D avatars of each body type of Korean women in their twenties and thirties which are verified with measurement suitability. The 3D anthropometric data of the Korean Anthropometric Survey (6th Size Korea) conducted in 2010 was used in this study. The collected subjects were 410 Korean women in their twenties and thirties. The 3D avatar modeling process using Maya 2013 was proposed to create the representative 3D avatars show superior measurement suitability. This process includes four steps; Analyzing body size measurements, 2D Image plane design, 3D avatar modeling, and 3D avatar evaluation. The 3D avatars created with this process showed the acceptable range of error. The factor analysis was performed on fifty-five body measurements chosen from the measurements of the 6th Size Korea anthropometric survey. Seven factors were extracted. With the seven extracted factors, body shapes of 406 Korean women in their twenties and thirties are classified into four groups by cluster analysis. The classified groups were named Full & Short, Slim & Short, Full & Tall, and Slim & Tall.     

Deformation similarity measurement in quasi-conformal shape space

This paper presents a novel approach based on the shape space concept to classify deformations of 3D models. A new quasi-conformal metric is introduced which measures the curvature changes at each vertex of each pose during the deformation. The shapes with similar deformation patterns follow a similar deformation curve in shape space. Energy functional of the deformation curve is minimized to calculate the geodesic curve connecting two shapes on the shape space manifold. The geodesic distance illustrates the similarity between two shapes, which is used to compute the similarity between the deformations. We applied our method to classify the left ventricle deformations of myopathic and control subjects, and the sensitivity and specificity of our method were 88.8% and 85.7%, which are higher than other methods based on the left ventricle cavity, which shows our method can quantify the similarity and disparity of the left ventricle motion well.     

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.     

Multilevel active registration for kinect human body scans: from low quality to high quality

Registration of 3D human body has been a challenging research topic for over decades. Most of the traditional human body registration methods require manual assistance, or other auxiliary information such as texture and markers. The majority of these methods are tailored for high-quality scans from expensive scanners. Following the introduction of the low-quality scans from cost-effective devices such as Kinect, the 3D data capturing of human body becomes more convenient and easier. However, due to the inevitable holes, noises and outliers in the low-quality scan, the registration of human body becomes even more challenging. To address this problem, we propose a fully automatic active registration method which deforms a high-resolution template mesh to match the low-quality human body scans. Our registration method operates on two levels of statistical shape models: (1) the first level is a holistic body shape model that defines the basic figure of human; (2) the second level includes a set of shape models for every body part, aiming at capturing more body details. Our fitting procedure follows a coarse-to-fine approach that is robust and efficient. Experiments show that our method is comparable with the state-of-the-art methods for high-quality meshes in terms of accuracy and it outperforms them in the case of low-quality scans where noises, holes and obscure parts are prevalent.     

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.     

Statistical shape analysis: clustering, learning, and testing

Using a differential-geometric treatment of planar shapes, we present tools for: 1) hierarchical clustering of imaged objects according to the shapes of their boundaries, 2) learning of probability models for clusters of shapes, and 3) testing of newly observed shapes under competing probability models. Clustering at any level of hierarchy is performed using a minimum variance type criterion and a Markov process. Statistical means of clusters provide shapes to be clustered at the next higher level, thus building a hierarchy of shapes. Using finite-dimensional approximations of spaces tangent to the shape space at sample means, we (implicitly) impose probability models on the shape space, and results are illustrated via random sampling and classification (hypothesis testing). Together, hierarchical clustering and hypothesis testing provide an efficient framework for shape retrieval. Examples are presented using shapes and images from ETH, Surrey, and AMCOM databases.     

FOLD PROFILER: A MATLAB®—based program for fold shape classification

FOLD PROFILER is a MATLAB code for classifying the shapes of profiles of folded surfaces. The classification is based on the comparison of the natural fold profile with curves representing mathematical functions. The user is offered a choice of four methods, each based on a different type of function: cubic Bezier curves, conic sections, power functions and superellipses. The comparison is carried out by the visual matching of the fold profile displayed on-screen from an imported digital image and computed theoretical curves which are superimposed on the image of the fold. To improve the fit with the real fold shape, the parameters of the theoretical curves are changed by simple mouse actions. The parameters of the mathematical function that best fits the real folds are used to classify the fold shape.FOLD PROFILER allows the rapid implementation of four existing methods for fold shape analysis. The attractiveness of this analytical tool lies in the way it gives an instant visual appreciation of the effect of changing the parameters that are used to classify fold geometry.