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 reviewe

Evaluation of Morphed Human Body Models for Diverse Occupant Safety Analysis

Female, obese, and elderly occupants are at increased risk of injury in vehicle accidents. Human Body Models (HBMs) are used to represent the human anatomy and to study injury mechanisms in mathematical crash test simulations. HBM morphing methods can adjust the anatomical geometry of existing HBMs, enabling HBMs to represent the diverse occupant population, beyond the traditionally considered body sizes.The aims of this thesis were to define and select a diverse population of occupants. Thereafter, select an HBM morphing tool for morphing of the SAFER HBM to individuals in this population; Finally, this population of morphed HBMs was to be validated.The defined target population to be represented by HBMs in occupant injury risk evaluations included individuals of both sexes. The selection was based on occupant injury risks and biomechanical risk factors. The male and female sub-populations include individuals of a wide range of statures and weights and ages from 20 to 80 years. A sample of 27 female and 27 males were selected as the initial population.The parametric HBM morphing tool, developed by University of Michigan Transportation Research Institute, was selected for morphing the SAFER HBM. Sled test results from individual male and female Post Mortem Human Subjects (PMHSs) of a wide range of body sizes were used for validation of morphed HBMs. The SAFER HBM was parametrically morphed to each individual PMHS. Predictions from both morphed and the baseline SAFER HBM were collected in reconstructions of the PMHS tests. HBM kinematics, chest deflections and interaction forces were compared to corresponding test results using CORA cross-correlation rating. Comparison of morphed and baseline HBM results showed that correlation rating was not consistently improved for morphed HBMs. For large, obese, and small female subjects in frontal impacts, and in lateral impacts, morphed HBMs were stiffer than the corresponding PMHSs. \ua0To improve morphed SAFER HBM predictions for diverse occupants, future work will identify and mitigate the sources of the stiff responses through model updates. Sex and age dependent biomechanical properties, as available in literature will be included. Biofidelity criteria for morphed HBMs will be defined and with morphed HBMs meeting these criteria, protective principles increasing the protection of all occupants will be investigated

Estimation of Human Body Shape and Posture Under Clothing

Estimating the body shape and posture of a dressed human subject in motion represented as a sequence of (possibly incomplete) 3D meshes is important for virtual change rooms and security. To solve this problem, statistical shape spaces encoding human body shape and posture variations are commonly used to constrain the search space for the shape estimate. In this work, we propose a novel method that uses a posture-invariant shape space to model body shape variation combined with a skeleton-based deformation to model posture variation. Our method can estimate the body shape and posture of both static scans and motion sequences of dressed human body scans. In case of motion sequences, our method takes advantage of motion cues to solve for a single body shape estimate along with a sequence of posture estimates. We apply our approach to both static scans and motion sequences and demonstrate that using our method, higher fitting accuracy is achieved than when using a variant of the popular SCAPE model as statistical model.Comment: 23 pages, 11 figure

Anthropometric rigging for variable manikin appearance

DHM (Digital Human Modeling) tools have increasingly become a contributor to human factors engi-neering and user-centered design. Sources report however that little attention has been paid to the appear-ance of DHM virtual human models, i.e. manikins. Aspects of visual appearance have considerable im-pact on conveying beliefs and attitudes, something often used in industrial design to motivate solutions. This work aims to synthesis the need of correct anthropometric representation in DHM with the artistic freedom available in visual art industries and address the need of more humanlike appearances of manikins

Skeleton-aware size variations in digital mannequins

The general trend in character modeling is toward the personalization of models with higher levels of visual realism. This becomes possible with the development of commodity computation resources that are capable of processing massive data in parallel across multiple processors. On the other hand, there is always a trade-off between the quantity of the model features that are simulated and the plausibility of the visual realism because of the limited computation resources. Also, to keep the resources' to be used efficiently within the other modeling approaches such as skin reflectance, aging, animation, etc., one must consider the efficiency of the method being used in the simulation. In this paper, we present an efficient method to customize the size of a human body model to personalize it with industry standard parameters. One of the major contributions of this method is that it is possible to generate a range of different size body models by using anthropometry surveys. This process is not limited by data-driven mesh deformation but also adapts the skeleton and motion to keep the consistency between different body layer

Prediction of 3D Body Parts from Face Shape and Anthropometric Measurements

While 3D body models have been vastly studied in the last decade, acquiring accurate models from the sparse information about the subject and few computational resources is still a main open challenge. In this paper, we propose a methodology for finding the most relevant anthropometric measurements and facial shape features for the prediction of the shape of an arbitrary segmented body part. For the evaluation, we selected 12 features that are easy to obtain or measure including age, gender, weight and height; and augmented them with shape parameters extracted from 3D facial scans. For each subset of features, with and without facial parameters, we predicted the shape of 5 segmented body parts using linear and non-linear regression models. The results show that the modeling approach is effective and giving sub cm reconstruction accuracy. Moreover, adding face shape features always significantly improves the prediction

Investigation of Optimization Targets for Predictive Simulation of Human Gait with Model Predictive Control

The design and development of gait-related treatments and devices is inhibited by anabsence of predictive gait models. Understanding of human gait and what motivates walkingpatterns is still limited, despite walking being one of the most routine human activities. While asignificant body of literature exists on gait modeling and optimization criteria to achievesimulated, normal gait, particularly with neuromuscular models, few studies have aimed to applyoptimization targets which approximate metabolic cost to mechanical gait models. Even fewerhave attempted this predictively, with no joint angle data specified a priori. The Sunmodel [31], [32] is one such mechanical framework which utilizes MPC to predict the dynamics ofhuman walking. This thesis expands the Sun model [31], [32] to simulate a full gait cycle (CG) andinvestigates the application of new optimization targets within an existing Model PredictiveControl (MPC) framework for predictive gait simulation developed by Sun [31], [32] .The Sun model [31], [32] was previously limited to a half gait cycle (GC) which assumedbilateral symmetry and optimized only according to characteristic constraints such as step lengthand velocity of the center of mass (COM). In this thesis, the Sun framework and MPC controlscheme were expanded to generate consecutive double support (DS), single support (SS), DS, andSS period simulations, which constitutes a full GC. The resulting GC simulation was not markedby GC events toe off (TO) and heel strike (HS), but did achieve continuity over the period whichwas not achieved by the Sun model [31], [32] . Additionally, new cost functions were developedconsistent with existing literature which suggests that the Central Nervous System (CNS) uses avariety of energy-related targets in generating gait. This thesis demonstrates that the applicationof optimization targets which approximate metabolic costs is possible with the proposed MPCframework for a mechanical gait model, but that the performance of resulting simulations shouldnot be evaluated until a full GC marked by TO and HS is achieved.While a continuous full GC simulation was achieved, the failure of the model to reliablymeet characteristic constraints, particularly in SS, prevents simulation of a GC marked by TO andHS. The work in this thesis points primarily to the failure of the optimization routine within theMPC framework to reliably find a solution that meets constraints as the cause of this problem. Ifthe optimization problem can be classified, an appropriate solution algorithm could be chosenwhich could reliably find a solution for any given set of constraints and initial conditions (IC).Identifying an appropriate solution algorithm could make the MPC framework proposed a viablemethod of gait prediction and simulation.This investigation provides researchers better understanding of the application ofenergy-based optimization in mechanical gait models and the current limitations of gaitprediction and simulation. In addition, direction is given to the future work necessary to establishMPC as a viable control method for gait simulation

Real-Time Virtual Humans

The last few years have seen great maturation in the computation speed and control methods needed to portray 30 virtual humans suitable for real interactive applications. We first describe the state of the art, then focus on the particular approach taken at the University of Pennsylvania with the Jack system. Various aspects of real-time virtual humans are considered, such as appearance and motion, interactive control, autonomous action, gesture, attention, locomotion, and multiple individuals. The underlying architecture consists of a sense-control-act structure that permits reactive behaviors to be locally adaptive to the environment, and a PaT-Net parallel finite-state machine controller that can be used to drive virtual humans through complex tasks. We then argue for a deep connection between language and animation and describe current efforts in linking them through two systems: the Jack Presenter and the JackMOO extension to lambdaM00. Finally, we outline a Parameterized Action Representation for mediating between language instructions and animated actions