Body Shape Model Research Articles

The influence of the aesthetic body shape model on adolescents with eating disorders.

the relevance of sociocultural factors with respect to etiology, development and treatment of eating disorders has been supportedby many studies. the aims of this study were: a) to analyze the different effects of the aesthetic body shape model on adolescents with anorexia nervosa(AN) vs bulimia nervosa (BN); b) to analyze possible differences, regarding that body shape model, between patients with purging behaviors vsnon purging types of patients; and c) to explore the relationship between the influence of the aesthetic body shape model and other clinicallyrelevant variables such as body dissatisfaction, eating attitudes and personality traits. the sample comprised 104 adolescents suffering from AN and BN. The Questionnaire of Influences on the Aesthetic Body ShapeModel (CIMEC-40), the Body Shape Questionnaire (BSQ), the Eating Attitudes Test (EAT-40) and the Millon Adolescent Clinical Inventory (MACI)were applied. the aesthetic body shape model of thinness influenced 77.9% of this sample, this influence being higher in the case of BN patients.In addition, that influence was stronger in the purging-type patients than in the non-purging type. Moreover, there was a significant and positivecorrelation among the influence of the aesthetic body shape model, body dissatisfaction and severity of eating symptoms. Finally, there seemsto exist some personality traits more vulnerable to be affected by sociocultural factors. in view of these results, it is necessary that psychotherapeutic approaches take into account the influence of sociocultural factorsand body dissatisfaction mainly in the case of adolescents with BN.

Automatic liver segmentation in computed tomography using general-purpose shape modeling methods

BackgroundLiver segmentation in computed tomography is required in many clinical applications. The segmentation methods used can be classified according to a number of criteria. One important criterion for method selection is the shape representation of the segmented organ. The aim of the work is automatic liver segmentation using general purpose shape modeling methods.MethodsAs part of the research, methods based on shape information at various levels of advancement were used. The single atlas based segmentation method was used as the simplest shape-based method. This method is derived from a single atlas using the deformable free-form deformation of the control point curves. Subsequently, the classic and modified Active Shape Model (ASM) was used, using medium body shape models. As the most advanced and main method generalized statistical shape models, Gaussian Process Morphable Models was used, which are based on multi-dimensional Gaussian distributions of the shape deformation field.ResultsMutual information and sum os square distance were used as similarity measures. The poorest results were obtained for the single atlas method. For the ASM method in 10 analyzed cases for seven test images, the Dice coefficient was above 55%, of which for three of them the coefficient was over 70%, which placed the method in second place. The best results were obtained for the method of generalized statistical distribution of the deformation field. The DICE coefficient for this method was 88.5%ConclusionsThis value of 88.5 % Dice coefficient can be explained by the use of general-purpose shape modeling methods with a large variance of the shape of the modeled object—the liver and limitations on the size of our training data set, which was limited to 10 cases. The obtained results in presented fully automatic method are comparable with dedicated methods for liver segmentation. In addition, the deforamtion features of the model can be modeled mathematically by using various kernel functions, which allows to segment the liver on a comparable level using a smaller learning set.

3D Reconstruction of Scoliotic Spines from Stereoradiography and Depth Imaging.

Spine shape can be reconstructed from stereoradiography, but often requires specialized infrastructure or fails to account for subject posture. In this paper a protocol is presented for stereo reconstructions that integrates surface recordings with radiography and naturally accounts for variations in patient posture. Low cost depth cameras are added to an existing radiographic system to capture patient pose. A statistical model of human body shape is learned from public datasets and registered to depth scans, providing 3D correspondence across images for stereo reconstruction of radiographic landmarks. A radiographic phantom was used to validate these methods in vitro with RMS 3D landmark reconstruction error of 2.0mm. Surfaces were automatically and reliably registered, with SD 12mm translation disparity and SD .5° rotation. The proposed method is suitable for 3D radiographic reconstructions and may be beneficial in compensating for involuntary patient motion.

Three Dimensional Shapes Analysis for Digital Fashion: Relation among Women's Trunk, Breast, and Abdomen

A novel method of analyzing body shapes of people based on three-dimensional (3D) measurements is proposed. The method is to analyze the body shape by combining analysis results of body parts in order to avoid size and posture affecting the results. To this aim, our previous studies have analyzed the trunk, breast, and abdomen of about 500 Japanese women and extracted their shape factors by combining a body shape model and a principal component analysis. The model describes the trunk of a subject with 750 control points on a B-spline surface normalized by seventeen landmarks and enables us to treat the 3D body shape mathematically, including calculation of average shape. The extracted shape factors comprised six of the trunk, four of the breast, and four of the abdomen. An interpretation of the factors was also performed. The relation among 3D shapes of the trunk, breast, and abdomen is examined in this paper. Component scores of the 536 subjects and interpretations of the fourteen shape factors are used. A correlation matrix and a correlation diagram evaluate the relation of sixty-four pairs of shape factors between the different body parts. The average shapes of the subjects that are classified by the scores illustrate/express the difference in 3D shape of each pair of shape factors. Fifteen of the pairs with correlation coefficients (r) of more than 0.30 in absolute value are focused on and discussed. As a result, two of the pairs, reflecting breast height and degree of obesity, were found to have correlation coefficients sufficiently high (r=0.81,-0.53 respectively) and have similar interpretations. On the other hand, twelve of the fourteen factors have different meanings. This implies that different parts of the body have different shape factors and twelve or more parameters are necessary when treating the 3D shape in a simple way. In addition, some of the other thirteen pairs show tendencies of the 3D body shapes of the subjects. It has been confirmed that the shapes of body parts tend to change with posture, the shape of other parts, age, stature, and BMI. For instance, the level of the shoulder slope appears to affect the appearance of the breast. This paper also summarizes our series of studies on body shape analysis. Necessity, generalizability, limitation of the presented method, and application to other body parts are discussed.

Parametric modeling of 3D human body shape—A survey

Parametric modeling of 3D body shape is widely used to create realistic human bodies. It furthermore permits robust reconstruction of complete 3D body shapes even from incomplete capture data—traditional scanning techniques result in gaps and missing regions due to occlusion and inaccessibility of certain areas of the body. Numerous methods of parametric modeling have been proposed for a wide variety of 3D body processing tasks. They provide the ability to represent a range of identity-dependent body shapes, and to deform them naturally into various poses. This report surveys and classifies recent developments in parametric 3D body shape modeling. We focus on elucidating the key similarities and differences between existing methods as an aid to understanding their relationships. We also discuss a variety of 3D body shape processing applications that benefit from parametric modeling. Our analysis of the strengths and limitations of existing algorithms also lets us highlight opportunities for future research.

Embodied hands

Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO ( hand Model with Articulated and Non-rigid defOrmations ). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes at http://mano.is.tue.mpg.de.

A parametric model of child body shape in seated postures

ABSTRACTObjective: The shape of the current physical and computational surrogates of children used for restraint system assessments is based largely on standard anthropometric dimensions. These scalar dimensions provide valuable information on the overall size of the individual but do not provide good guidance on shape or posture. This study introduced the development of a parametric model that statistically predicts individual child body shapes in seated postures with a few given parameters.Methods: Surface geometry data from a laser scanner of children ages 3 to 11 (n = 135) were standardized by a 2-level fitting method using intermediate templates. The standardized data were analyzed by principal component analysis (PCA) to efficiently describe the body shape variance. Parameters such as stature, body mass index, erect sitting height, and 2 posture variables related to torso recline and lumbar spine flexion were associated with the PCA model using regression.Results: When the original scan data were compared with the predictions of the model using the given subject dimensions, the average root mean square error for the torso was 9.5 mm, and the 95th percentile error was 17.35 mm.Conclusions: For the first time, a statistical model of child body shapes in seated postures is available. This parametric model allows the generation of an infinite number of virtual children spanning a wide range of body sizes and postures. The results have broad applicability in product design and safety analysis. Future work is needed to improve the representation of hands and feet and to extend the age range of the model. The model presented in this article is publicly available online through HumanShape.org.

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.

Impulsivity and eating behavior in males

Introduction: Impulsivity is a personality trait related with the control of behaviour and emotions and it is found in different psychopathological alterations, including those referred to eating behaviour. Objective: The aim of this study was to analyse the relationship among the infl uence of the aesthetic body shape model, eating behaviour (and risk for specific disorders), body mass index and impulsivity. Method: A total of 178 males were included in the study, with a mean age of 20.18 ± 2.48. Height and weight were assessed in order to obtain the body mass index. All participants fulfilled the following questionnaires: Barratt Impulsivity Scale (BIS-11), Questionnaire of Infl uences on the Body Shape Model for males (CIMEC-V) and the Eating Attitudes Test (EAT-40). Results: With respect to possible cases of eating disorders, 5.06% were found. Scores of some items of BIS-11 correlated significantly with the EAT-40 and CIMEC-V scores. Overweight participants (39.89%) showed higher level of impulsivity as well as those with EAT-40 scores above the cut-off point for that test. Conclusions: The assessment of impulsivity from a psychological point of view might be a preventive tool with regards to disordered eating behaviours. Respecting the patients with eating disorders/overweight/obesity, that assessment might be a relevant aspect in order to improve the therapeutical approach.

Body dissatisfaction in adolescents with eating disorders.

This study aims to analyze and compare the level of body dissatisfaction (BD) in different eating disorders (ED) subtypes. Also, the relationship between BD and certain aesthetic body shape model influence and psychological variables was analyzed. The sample consisted of 204 adolescent patients, who were attending in an ED Unit in Zaragoza (Spain). The following instruments were applied: the Spanish Children's Depression Questionnaire (CEDI-II), the Rosenberg Self-Esteem Scale (RSES), the Eating Attitudes Test (EAT-40), the Body Shape Questionnaire (BSQ-34) and the Questionnaire of Influences of Aesthetic Body shape Model (CIMEC-40). The group of Bulimia Nervosa (BN) showed the greatest BD. Those patients who showed higher levels of BD had lower self-esteem, more depressive symptoms, a greater presence of disordered eating attitudes, and more influence of the aesthetic body shape model. This study contributes to highlight the idea of implementing effective prevention programs and specific interventions related to BD in the treatment of ED.

Determination of a new uniform thorax density representative of the living population from 3D external body shape modeling

Body segment parameters (BSP) for each body׳s segment are needed for biomechanical analysis. To provide population-specific BSP, precise estimation of body׳s segments volume and density are needed. Widely used uniform densities, provided by cadavers׳ studies, did not consider the air present in the lungs when determining the thorax density. The purpose of this study was to propose a new uniform thorax density representative of the living population from 3D external body shape modeling. Bi-planar X-ray radiographies were acquired on 58 participants allowing 3D reconstructions of the spine, rib cage and human body shape. Three methods of computing the thorax mass were compared for 48 subjects: (1) the Dempster Uniform Density Method, currently in use for BSPs calculation, using Dempster density data, (2) the Personalized Method using full-description of the thorax based on 3D reconstruction of the rib cage and spine and (3) the Improved Uniform Density Method using a uniform thorax density resulting from the Personalized Method. For 10 participants, comparison was made between the body mass obtained from a force-plate and the body mass computed with each of the three methods. The Dempster Uniform Density Method presented a mean error of 4.8% in the total body mass compared to the force-plate vs 0.2% for the Personalized Method and 0.4% for the Improved Uniform Density Method. The adjusted thorax density found from the 3D reconstruction was 0.74g/cm3 for men and 0.73g/cm3 for women instead of the one provided by Dempster (0.92g/cm3), leading to a better estimate of the thorax mass and body mass.

SMPL

We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity-dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. We quantitatively evaluate variants of SMPL using linear or dual-quaternion blend skinning and show that both are more accurate than a Blend-SCAPE model trained on the same data. We also extend SMPL to realistically model dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

A Three-Dimensional Skeletal Reconstruction of the Stem Amniote Orobates pabsti (Diadectidae): Analyses of Body Mass, Centre of Mass Position, and Joint Mobility

Orobates pabsti, a basal diadectid from the lower Permian, is a key fossil for the understanding of early amniote evolution. Quantitative analysis of anatomical information suffers from fragmentation of fossil bones, plastic deformation due to diagenetic processes and fragile preservation within surrounding rock matrix, preventing further biomechanical investigation. Here we describe the steps taken to digitally reconstruct MNG 10181, the holotype specimen of Orobates pabsti, and subsequently use the digital reconstruction to assess body mass, position of the centre of mass in individual segments as well as the whole animal, and study joint mobility in the shoulder and hip joints. The shape of most fossil bone fragments could be recovered from micro-focus computed tomography scans. This also revealed structures that were hitherto hidden within the rock matrix. However, parts of the axial skeleton had to be modelled using relevant isolated bones from the same locality as templates. Based on the digital fossil, mass of MNG 10181 was estimated using a model of body shape that was varied within a plausible range to account for uncertainties of the dimension. In the mean estimate model the specimen had an estimated mass of circa 4 kg. Varying of the mass distribution amongst body segments further revealed that Orobates carried most of its weight on the hind limbs. Mostly unrestricted joint morphology further suggested that MNG 10181 was able to effectively generate propulsion with the pelvic limbs. The digital reconstruction is made available for future biomechanical studies.

Example-based statistical framework for parametric modeling of human body shapes

This paper proposes a new example-based statistical modeling framework to conduct the parametric modeling of 3D human body shapes from linear anthropometric parameters. The modeling framework consists of the following three phases: construction of a training database of human body shapes, statistical analysis of human body shapes and human body shape modeling. In the training database construction phase, a consistent parameterization was carried out on 3D whole-body scan data of 80 males and 80 females with a wide variety of body shapes as the examples for this study. The surface-fitting process, which was improved relative to existing methods, was used to guarantee the high-quality parameterization in this phase and to generate an articulated body shape model in the modeling phase. To characterize the range of body shape variation, the training database was analyzed statistically. Additionally, a correlation between the body shapes and the anthropometric parameters was learned in the statistical analysis phase to estimate body shapes from intuitive and semantic parameters. A new technique to generate an optimal body shape model that precisely satisfies user input body dimensions was developed in the model generation phase. This technique enables the estimation of body shape variation, not only within the body shape space that was learned statistically, but also outside of the body shape space, while maintaining body shapes that stay in the human shape space. Our approach produced reasonable results having a high modeling accuracy satisfying user-specified anthropometric parameters, and high visual quality in expressing realistic body shapes. The resultant models were then segmented into 16 key regions of the human body, and had information on 15 key joints, and thus they could be a useful tool in various industries. Compared with other statistical modeling approaches, the proposed method contributes to related areas by introducing an improved surface-fitting process and a non-linear optimization-based optimal body shape modeling technique.

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.

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.

Can I Recognize My Body's Weight? The Influence of Shape and Texture on the Perception of Self

The goal of this research was to investigate women's sensitivity to changes in their perceived weight by altering the body mass index (BMI) of the participants' personalized avatars displayed on a large-screen immersive display. We created the personalized avatars with a full-body 3D scanner that records the participants' body geometry and texture. We altered the weight of the personalized avatars to produce changes in BMI while keeping height, arm length, and inseam fixed and exploited the correlation between body geometry and anthropometric measurements encapsulated in a statistical body shape model created from thousands of body scans. In a 2 × 2 psychophysical experiment, we investigated the relative importance of visual cues, namely shape (own shape vs. an average female body shape with equivalent height and BMI to the participant) and texture (own photo-realistic texture or checkerboard pattern texture) on the ability to accurately perceive own current body weight (by asking the participant, “Is it the same weight as you?”). Our results indicate that shape (where height and BMI are fixed) had little effect on the perception of body weight. Interestingly, the participants perceived their body weight veridically when they saw their own photo-realistic texture. As compared to avatars with photo-realistic texture, the avatars with checkerboard texture needed to be significantly thinner in order to represent the participants' current weight. This suggests that in general the avatars with checkerboard texture appeared bigger. The range that the participants accepted as their own current weight was approximately a 0.83% to − 6.05% BMI change tolerance range around their perceived weight. Both the shape and the texture had an effect on the reported similarity of the body parts and the whole avatar to the participant's body. This work has implications for new measures for patients with body image disorders, as well as researchers interested in creating personalized avatars for games, training applications, or virtual reality.

Parametric model of human body shape and ligaments for patient-specific epidural simulation.

This work is to build upon the concept of matching a person's weight, height and age to their overall body shape to create an adjustable three-dimensional model. A versatile and accurate predictor of body size and shape and ligament thickness is required to improve simulation for medical procedures. A model which is adjustable for any size, shape, body mass, age or height would provide ability to simulate procedures on patients of various body compositions. Three methods are provided for estimating body circumferences and ligament thicknesses for each patient. The first method is using empirical relations from body shape and size. The second method is to load a dataset from a magnetic resonance imaging (MRI) scan or ultrasound scan containing accurate ligament measurements. The third method is a developed artificial neural network (ANN) which uses MRI dataset as a training set and improves accuracy using error back-propagation, which learns to increase accuracy as more patient data is added. The ANN is trained and tested with clinical data from 23,088 patients. The ANN can predict subscapular skinfold thickness within 3.54 mm, waist circumference 3.92 cm, thigh circumference 2.00 cm, arm circumference 1.21 cm, calf circumference 1.40 cm, triceps skinfold thickness 3.43 mm. Alternative regression analysis method gave overall slightly less accurate predictions for subscapular skinfold thickness within 3.75 mm, waist circumference 3.84 cm, thigh circumference 2.16 cm, arm circumference 1.34 cm, calf circumference 1.46 cm, triceps skinfold thickness 3.89 mm. These calculations are used to display a 3D graphics model of the patient's body shape using OpenGL and adjusted by 3D mesh deformations. A patient-specific epidural simulator is presented using the developed body shape model, able to simulate needle insertion procedures on a 3D model of any patient size and shape. The developed ANN gave the most accurate results for body shape, size and ligament thickness. The resulting simulator offers the experience of simulating needle insertions accurately whilst allowing for variation in patient body mass, height or age.

Creating Custom Human Avatars for Ergonomic Analysis using Depth Cameras

Ergonomic analysis of industrial tasks is often conducted through the use of human figure modeling software, such as the Jack software from Siemens. Typically, figures are scaled by inputting overall anthropometric dimensions, such as stature, body weight, and erect sitting height. This paper presents a method for rapidly generating a figure that matches the body dimensions and shape of an individual. A system with two Microsoft Kinect depth cameras is used to gather shape data. A statistical body shape model (SBSM) generated from analysis of over 250 men and women is used to fit the data. The output of the shape fitting, expressed as a set of 20 principal component scores, is input to the same male or female SBSM implemented in the Jack software. The result is a figure model that closely matches both the size and shape of the scanned individual. This methodology will be useful for a range of applications for which having a customized manikin is advantageous.

法線に基づく形状特徴を用いた睡眠時における人物姿勢推定

In this paper, we present a robust pose estimation method of a human body hidden with a cover. Our goal is an automatically monitoring system of sleeping human using noncontact and noninvasive sensors for elderly care. We propose a new method for robust human pose estimation from a single depth image using human body shape model constructed by normal vector information. This shape model is able to represent shapes of rough body, and is effective in robust pose estimation for a person who placed futon and blanket. In our method, first, head position is detected from a depth image using SVM. Then, body region is detected by comparing human body shape model. Head position is used as initial position of body region detecting. Body region is composed of many small rectangles. Next, body region is divided into three body parts by distance between parts. Finally, each part pose is determined by a linear estimation using point clouds of right and left of the rectangle in each body region.