Statistical Body Shape Model Research Articles

Deep generation of 3D articulated models and animations from 2D stick figures

Generating 3D models from 2D images or sketches is a widely studied important problem in computer graphics. We describe the first method to generate a 3D human model from a single sketched stick figure. In contrast to the existing human modeling techniques, our method does not require a statistical body shape model. We exploit Variational Autoencoders to develop a novel framework capable of transitioning from a simple 2D stick figure sketch, to a corresponding 3D human model. Our network learns the mapping between the input sketch and the output 3D model. Furthermore, our model learns the embedding space around these models. We demonstrate that our network can generate not only 3D models, but also 3D animations through interpolation and extrapolation in the learned embedding space. In addition to 3D human models, we produce 3D horse models in order to show the generalization ability of our framework. Extensive experiments show that our model learns to generate compatible 3D models and animations with 2D sketches.

A Parametric Modeling of Adult Body Shape in a Supported Seated Posture Including Effects of Age

ABSTRACT Statistical body shape models (SBSM) provide compact, flexible representations of body shape that can be implemented in design software. However, few SBSMs have been created to represent adults in supported seated postures that are relevant for the design of seated environments, and none has incorporated the effects of age. This paper presents an SBSM based on surface laser-scan data from 155 U.S. adults. The data were processed to obtain homologous mesh structure and symmetric geometry, and the processed data were statistically analyzed using principal component analysis to obtain a compact representation of the data variance. Regression analysis was conducted to predict body size and shape from stature, body mass index, ratio of sitting height to stature, sex, and age. The resulting model allows rapid generation of realistic body models for applications, including product design, accommodation assessment, and safety system optimization. The model is publicly accessible at HumanShape.org. PRACTITIONER SUMMARY: This paper presents a statistical model that represents adult body shapes in a supported seated posture based on 3D anthropometric measurements. This model is the first whole-body parametric model known to incorporate age effects based on data extending beyond 65 years of age.

Obesity effects on pedestrian lower extremity injuries in vehicle-to-pedestrian impacts: A numerical investigation using human body models

Objective The objectives of this study were to develop a method for modeling obese pedestrians and to investigate effects of obesity on pedestrian impact responses and injury outcomes. Methods The GHBMC (Global Human Body Model Consortium) 50th percentile male pedestrian model was morphed into geometries with 4 body mass index (BMI) levels (25/30/35/40 kg/m2) predicted by statistical body shape models. Each of the 4 morphed models was further morphed from a standing posture into 2 other gaits (toe-off and mid-swing), which resulted in a total of 12 (4 BMIs × 3 postures) models. Each model was used to simulate vehicle-to-pedestrian impact under 2 impact velocities. Pedestrian kinematics and injury measures were analyzed focusing on lower extremities. Statistical analyses were performed to examine significance of obesity on concerned injury measures. Results Peak values of the bending moment at tibia, force at medial collateral ligament (MCL), bending angle at knee joint, and contact force between vehicle and pedestrian increased significantly (P < .05) with increased BMI. By analyzing kinematics of the lower extremity, the overall vehicle-to-pedestrian impact was divided into 2 phases: “initial contact” and “tibia rebound.” For obese pedestrians, the added mass caused a higher tibia bending moment in the initial contact phase, and the greater moment of inertia led to greater bending angle and MCL force in the tibia rebound phase. Statistical analyses also revealed that pre-impact posture and impact velocity had significant effects on all injury measures. Conclusions Obesity could significantly increase the risk of pedestrian lower extremity injuries due to the inertial effect from the added mass. Pre-impact posture and impact velocity also significantly affect pedestrian injury measures. Future vehicle designs for pedestrian protection should consider populations with obesity. This study demonstrated the feasibility of using parametric human modeling to account for population diversity in injury prediction.

Statistical Human Body Shape Model including Elderly People.

In this study, we present a human body shape statistical model including elderly people, which is constructed using principal component analysis (PCA) on 3D body scan data of approximately 130 people. As a pre-process step, a template human body mesh model is fitted to 3D scan data using a coarse-to-fine surface registration technique based on a conformal deformation method, in order to establish correspondences between the scans of different subjects possibly in different poses. To change body style by a small set of parameters, such as "age", "weight" and "height" or the easily measurable anthropometric parameters like "shoulder width", the linear transformations between these attributes and the first 10 principal component scores are obtained. We design a simple user interface to use this deformation model to generate different body styles easily. As a result, we were able to produce and show body styles capturing the characteristics of elderly people whose shoulders fell and back bent. Finally, as an application, we used our deformation method to generate different body types, performed forward dynamics simulations in an assistive device setting and visualized the differences in contact pressure distributions due to body shape changes.

Model-based characterisation of vehicle occupants using a depth camera

Due to recent advances in sensing technologies, modern vehicle occupant classification systems enable personalised vehicle experiences and adaptive occupant crash protection. However, most systems are limited to occupant detection and simple classification, and thus, accurate estimation of body characteristics is needed to support more advanced occupant classification. This paper presents a model-based characterisation method for vehicle occupants using a 3D depth camera. This method automatically estimates standard anthropometric data of an occupant such as stature and weight along with the body shape by fitting a statistical body shape model to depth image data. The system is even robust to a wide range of clothing and is capable of generating accurate results. A variety of other algorithms were developed to improve the fitting result, including seat geometry detection and head location estimation. The new capability has a range of potential applications for improving occupant safety and providing an optimised interior configuration for the occupant.

A Novel Self-Intersection Penalty Term for Statistical Body Shape Models and Its Applications in 3D Pose Estimation

Statistical body shape models are widely used in 3D pose estimation due to their low-dimensional parameters representation. However, it is difficult to avoid self-intersection between body parts accurately. Motivated by this fact, we proposed a novel self-intersection penalty term for statistical body shape models applied in 3D pose estimation. To avoid the trouble of computing self-intersection for complex surfaces like the body meshes, the gradient of our proposed self-intersection penalty term is manually derived from the perspective of geometry. First, the self-intersection penalty term is defined as the volume of the self-intersection region. To calculate the partial derivatives with respect to the coordinates of the vertices, we employed detection rays to divide vertices of statistical body shape models into different groups depending on whether the vertex is in the region of self-intersection. Second, the partial derivatives could be easily derived by the normal vectors of neighboring triangles of the vertices. Finally, this penalty term could be applied in gradient-based optimization algorithms to remove the self-intersection of triangular meshes without using any approximation. Qualitative and quantitative evaluations were conducted to demonstrate the effectiveness and generality of our proposed method compared with previous approaches. The experimental results show that our proposed penalty term can avoid self-intersection to exclude unreasonable predictions and improves the accuracy of 3D pose estimation indirectly. Further more, the proposed method could be employed universally in triangular mesh based 3D reconstruction.

Development of a three-dimensional body shape model of young children for child restraint design

The design of child restraints is guided in part by anthropometric data describing the distributions of body dimensions of children. However, three-dimensional body shape data have not been available for children younger than three years of age. This study presents body shape models for children weighing 9–23 kg in a seated posture relevant to child restraint design. A laboratory study collected surface geometry data of 67 children, ages 12–58 months. Novel template fitting methods were employed to obtain homologous meshes and to standardize the posture. Principal component analysis and regression were used to develop a statistical body shape model (SBSM). The SBSM was exercised to create 18 manikins representing children aged 1–3 years, with varying size and shape. These manikins will be useful for assessing child accommodation in restraints. The SBSM can also provide guidance for the development of anthropomorphic test devices and computational models of child occupants.

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.

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.