Human Body Model Research Articles

Stretchable spring‐sheathed yarn sensor for 3D dynamic body reconstruction assisted by transfer learning

AbstractA wearable sensing system that can reconstruct dynamic 3D human body models for virtual cloth fitting is highly important in the era of information and metaverse. However, few research has been conducted regarding conformal sensors for accurately measuring the human body circumferences for dynamic 3D human body reshaping. Here, we develop a stretchable spring‐sheathed yarn sensor (SSYS) as a smart ruler, for precisely measuring the circumference of human bodies and long‐term tracking the movement for the dynamic 3D body reconstruction. The SSYS has a robust property, high resilience, high stability (>18 000), and ultrafast response (12 ms) to external deformation. It is also washable, wearable, tailorable, and durable for long‐time wearing. Moreover, geometric, and mechanical behaviors of the SSYS are systematically investigated both theoretically and experimentally. In addition, a transfer learning algorithm that bridges the discrepancy of real and virtual sensing performance is developed, enabling a small body circumference measurement error of 1.79%, noticeably lower than that of traditional learning algorithm. Furtherly, 3D human bodies that are numerically consistent with the actual bodies are reconstructed. The 3D dynamic human body reconstruction based on the wearing sensing system and transfer learning algorithm enables excellent virtual fitting and shirt customization in a smart and highly efficient manner. This wearable sensing technology shows great potential in human‐computer interaction, intelligent fitting, specialized protection, sports activities, and human physiological health tracking.image

Analysis of Magnetotherapy Device-Induced Fields Using Cylindrical Human Body Model

This paper deals with the analysis of induced current density and the induced electric field in the body of a human exposed to the magnetic field of a magnetotherapy device. As the displacement currents at extremely low frequencies can be neglected, the biological tissues can thus be considered a weakly conducting medium, facilitating the use of a quasi-static eddy current approximation. The formulation is based on the surface integral equation for the unknown surface charges, whose numerical solution is obtained using the method of moments technique. A simplified model of the human body is utilized to examine various scenarios during the magnetotherapy procedure. The numerical results for the induced current density and the induced electric field are obtained using the proposed model. The analyses of various stimulating coil parameters, human body model parameters, and a displacement of the magnetotherapy coil were carried out to assess their effects on the induced current density. The results suggest that selection of the stimulating coil should be matched based on the size of the human body, but also that the position and orientation of the coil with respect to the body surface will result in different distributions of the induced fields. The results of this study could be useful for medical professionals by showing the importance of various magnetotherapy coil parameters for preparation of various treatment scenarios.

Numerical analysis of injuries of e-scooter riders in frontal impacts against SUVs

The rising popularity of e-scooters in urban areas highlights the importance of understanding potential collision dynamics and consequences to enhance rider safety, especially since 80% of fatalities for these vulnerable road users result from incidents involving motor vehicles. This study aims to identify potential injury mechanisms of e-scooter riders involved in frontal impacts against Sports Utility Vehicles (SUVs) using Finite Element Analysis (FEA) and a Human Body Model (HBM). Six impact scenarios at 25 km/h were simulated: on the passenger's side door (1-SD), on the trunk door (2-TD), on the bonnet (3-FB), on the bonnet from the side (4-SB), on the windshield from the side (5-SW), and finally, on the bonnet with an offset (6-FO). A wide range of Injury Risk Criteria (IRCs) was analyzed, including A3MS, HIC15, BrIC, DAMAGE, Cmax and PC Score, and head contact forces were output. Injury Risk Curves were then used to calculate the probability and severity of the sustained injuries predicted by the mentioned IRCs. Overall, the results corresponding to the most injurious scenarios suggested serious (30% to 40% overall probability) and critical (45% probability for the 4-SB scenario) brain, moderate head (30% probability for scenarios 1-SD and 2-TD), and serious thoracic (26% to 78% overall probability) injuries. Additionally, in two scenarios (1-SD and 2-TD), the e-scooter rider's mandible impacted against the vehicle's roof side rail and rear spoiler, involving potential anterior-posterior serious mandible fractures. The conclusions reached in this study help to understand potential injury sources and provide valuable information for the design of specific safety systems or the creation or improvement of e-scooter legislation.

Self-avatar: Monocular 3D human reconstruction from RGB image

3D human position and shape estimate are crucial in many computer vision applications. Despite the fact that there are numerous deep learning techniques designed to handle this problem, they frequently only use training networks with RGB images from a single point of view. In this paper, a unique approach to solve this issue is proposed by combining a regression-based multi-view picture learning loop with an optimization-based multi-view model. This is because some public datasets are collected by multi-view camera systems. A parameterized human body model's position and shape parameters are initially deduced by a convolutional neural network (CNN) from multi-view photos. This work then introduces an enhanced multi-view optimization method called MV-SMPLify, which aligns the SMPL model with multi-view images by using the regressed pose and shape as beginning values. Following that, the CNN model's training can be monitored using the optimum parameters. The Self-avatar project as a whole is a self-supervised framework that combines the advantages of both the CNN method and the optimization-based strategy. Additionally, the use of multi-view photos improves thorough supervision during training. This methodology outperforms earlier methods in a variety of ways, according to qualitative and quantitative testing using open datasets.

Investigation on vehicle occupant dummy applicability for under-foot impact loading conditions

PurposeUnder-foot impact loadings can cause serious lower limb injuries in many activities, such as automobile collisions and underbody explosions to military vehicles. The present study aims to compare the biomechanical responses of the mainstream vehicle occupant dummies with the human body lower limb model and analyze their robustness and applicability for assessing lower limb injury risk in under-foot impact loading environments. MethodsThe Hybrid III model, the test device for human occupant restraint (THOR) model, and a hybrid human body model with the human active lower limb model were adopted for under-foot impact analysis regarding different impact velocities and initial lower limb postures. ResultsThe results show that the 2 dummy models have larger peak tibial axial force and higher sensitivity to the impact velocities and initial postures than the human lower limb model. In particular, the Hybrid III dummy model presented extremely larger peak tibial axial forces than the human lower limb model. In the case of minimal difference in tibial axial force, Hybrid III's tibial axial force (7.5 KN) is still 312.5% that of human active lower limb's (2.4 KN). Even with closer peak tibial axial force values, the biomechanical response curve shapes of the THOR model show significant differences from the human lower limb model. ConclusionBased on the present results, the Hybrid III dummy cannot be used to evaluate the lower limb injury risk in under-foot loading environments. In contrast, potential improvement in ankle biofidelity and related soft tissues of the THOR dummy can be implemented in the future for better applicability.

A First Step Toward a Family of Morphed Human Body Models Enabling Prediction of Population Injury Outcomes.

The injury risk in a vehicle crash can depend on occupant specific factors. Virtual crash testing using finite element human body models (HBMs) to represent occupant variability can enable the development of vehicles with improved safety for all occupants. In this study, it was investigated how many HBMs of different sizes that are needed to represent a population crash outcome through a metamodel. Rib fracture risk was used as an example occupant injury outcome. Morphed HBMs representing variability in sex, height, and weight within defined population ranges were used to calculate population variability in rib fracture risk in a frontal and a side crash. Two regression methods, regularized linear regression with second-order terms and Gaussian process regression (GPR), were used to metamodel rib fracture risk due to occupant variability. By studying metamodel predictive performance as a function of training data, it was found that constructing GPR metamodels using 25 individuals of each sex appears sufficient to model the population rib fracture risk outcome in a general crash scenario. Further, by utilizing the known outcomes in the two crashes, an optimization method selected individuals representative for population outcomes across both crash scenarios. The optimization results showed that 5-7 individuals of each sex were sufficient to create predictive GPR metamodels. The optimization method can be extended for more crashes and vehicles, which can be used to identify a family of HBMs that are generally representative of population injury outcomes in future work.

Biomechanical Response of Head Surrogate With and Without the Helmet.

Measurements of brain deformations under injurious loading scenarios are actively sought. In this work, we report experimentally measured head kinematics and corresponding dynamic, two-dimensional brain simulant deformations in head surrogates under a blunt impact, with and without a helmet. Head surrogates used in this work consisted of skin, skull, dura, falx, tentorium, and brain stimulants. The head surrogate geometry was based on the global human body models consortium's head model. A base head surrogate consisting of skin-skull-brain was considered. In addition, the response of two other head surrogates, skin-skull-dura-brain, and skin-skull-dura-brain-falx-tentorium, was investigated. Head surrogate response was studied for sagittal and coronal plane rotations for impactor velocities of 1 and 3 m/s. Response of head surrogates was compared against strain measurements in PMHS. The strain pattern in the brain simulant was heterogenous, and peak strains were established within ∼30 ms. The choice of head surrogate affect the spatiotemporal evolution of strain. For no helmet case, peak MPS of ∼50-60% and peak MSS of ∼35-50% were seen in brain simulant corresponding to peak rotational accelerations of ∼5000-7000 rad/s2. Peak head kinematics and peak MPS have been reduced by up to 75% and 45%, respectively, with the conventional helmet and by up to 90% and 85%, respectively, with the helmet with antirotational pads. Overall, these results provide important, new data on brain simulant strains under a variety of loading scenarios-with and without the helmets.

Lower Extremity Injury Risk Curve Development for a Human Body Model in the Underbody Blast Environment.

Computational human body models (HBMs) provide the ability to explore numerous candidate injury metrics ranging from local strain based criteria to global combined criteria such as the Tibia Index. Despite these efforts, there have been relatively few studies that focus on determining predicted injury risk from HBMs based on observed postmortem human subjects (PMHS) injury data. Additionally, HBMs provide an opportunity to construct risk curves using measures that are difficult or impossible to obtain experimentally. The Global Human Body Models Consortium (GHBMC) M50-O v 6.0 lower extremity was simulated in 181 different loading conditions based on previous PMHS tests in the underbody blast (UBB) environment and 43 different biomechanical metrics were output. The Brier Metric Score were used to determine the most appropriate metric for injury risk curve development. Using survival analysis, three different injury risk curves (IRC) were developed: "any injury," "calcaneus injury," and "tibia injury." For each injury risk curve, the top three metrics selected using the Brier Metric Score were tested for significant covariates including boot use and posture. The best performing metric for the "any injury," "calcaneus injury" and "tibia injury" cases were calcaneus strain, calcaneus force, and lower tibia force, respectively. For the six different injury risk curves where covariates were considered, the presence of the boot was found to be a significant covariate reducing injury risk in five out of six cases. Posture was significant for only one curve. The injury risk curves developed from this study can serve as a baseline for model injury prediction, personal protective equipment (PPE) evaluation, and can aid in larger scale testing and experimental protocols.

Translational medical bioengineering research of traumatic brain injury among Chinese and American pedestrians caused by vehicle collision based on human body finite element modeling

Based on the average human body size in China and the THUMS AM50 finite element model of the human body, the Kriging interpolation algorithm was used to model the Chinese 50th percentile human body, and the biological fidelity of the model was verified. We built three different types of passenger vehicle models, namely, sedan, sports utility vehicle (SUV), and multi-purpose vehicle (MPV), and used mechanical response analysis and finite element simulation to compare and analyze the dynamic differences and head injury differences between the Chinese 50th percentile human body and the THUMS AM50 model during passenger vehicle collisions. The results showed that there are obvious differences between the Chinese mannequin and THUMS in terms of collision time, collision position, invasion speed, and angle. When a sedan collided with the mannequins, the skull damage to the Chinese human body model was more severe, and when a sedan or SUV collided, the brain damage to the Chinese human body was more severe. The abovementioned results suggest that the existing C-NCAP pedestrian protection testing regulations may not provide the best protection for Chinese human bodies, and that the regulations need to be improved by combining collision damage mechanisms and the physical characteristics of Chinese pedestrians. This thorough investigation is positioned to shed light on the fundamental biomechanics and injury mechanisms at play. Furthermore, the amalgamation of clinically rooted translational and engineering research in the realm of traumatic brain injury has the potential to establish a solid foundation for discerning preventive methodologies. Ultimately, this endeavor holds the potential to introduce effective strategies aimed at preventing and safeguarding against traumatic brain injuries.

SpasticSim: a synthetic data generation method for upper limb spasticity modelling in neurorehabilitation

In neurorehabilitation, assessment of functional problems is essential to define optimal rehabilitation treatments. Usually, this assessment process requires distinguishing between impaired and non-impaired behavior of limbs. One of the common muscle motor disorders affecting limbs is spasticity, which is complicated to quantify objectively due to the complex nature of motor control. Thus, the lack of heterogeneous samples of patients constituting an acceptable amount of data is an obstacle which is relevant to understanding the behavior of spasticity and, consequently, quantifying it. In this article, we use the 3D creation suite Blender combined with the MBLab add-on to generate synthetic samples of human body models, aiming to be as sufficiently representative as possible to real human samples. Exporting these samples to OpenSim and performing four specific upper limb movements, we analyze the muscle behavior by simulating the six degrees of spasticity contemplated by the Modified Ashworth Scale (MAS). The complete dataset of patients and movements is open-source and available for future research. This approach advocates the potential to generate synthetic data for testing and validating musculoskeletal models.

Mechanical response of human thoracic spine ligaments under quasi-static loading: An experimental study

PurposeThis study aimed to investigate the geometrical and mechanical properties of human thoracic spine ligaments subjected to uniaxial quasi-static tensile test. MethodsFour human thoracic spines, obtained through a body donation program, were utilized for the study. The anterior longitudinal ligament (ALL), posterior longitudinal ligament (PLL), capsular ligament (CL), ligamenta flava (LF), and the interspinous ligament and supraspinous ligament complex (ISL + SSL), were investigated. The samples underwent specimen preparation, including dissection, cleaning, and reinforcement, before being immersed in epoxy resin. Uniaxial tensile tests were performed using a custom-designed mechanical testing machine equipped with an environmental chamber (T = 36.6 °C; humidity 95%). Then, the obtained tensile curves were averaged preserving the characteristic regions of typical ligaments response. ResultsGeometrical and mechanical properties, such as initial length and width, failure load, and failure elongation, were measured. Analysis of variance (ANOVA) revealed significant differences among the ligaments for all investigated parameters. Pairwise comparisons using Tukey's post-hoc test indicated differences in initial length and width. ALL and PLL exhibited higher failure forces compared to CL and LF. ALL and ISL + SSL demonstrated biggest failure elongation. Comparisons with other studies showed variations in initial length, failure force, and failure elongation across different ligaments. The subsystem (Th1 – Th6 and Th7 – Th12) analysis revealed increases in initial length, width, failure force, and elongation for certain ligaments. ConclusionsVariations of both the geometric and mechanical properties of the ligaments were noticed, highlighting their unique characteristics and response to tensile force. Presented results extend very limited experimental data base of thoracic spine ligaments existing in the literature. The obtained geometrical and mechanical properties can help in the development of more precise human body models (HBMs).

Material Properties of Fiber Bundles of the Superficial Medial Collateral Ligament of the Knee Joint.

The superficial medial collateral ligament (sMCL) of the human knee joint has functionally separate anterior and posterior fiber bundles. The two bundles are alternatively loaded as the knee flexion angle changes during walking. To date, the two bundles are usually not distinguished in knee ligament simulations because there has been little information about their material properties. In this study, we conducted quasi-static tensile tests on the sMCL of matured porcine stifle joints and obtained the material properties of the anterior bundle (AB), posterior bundle (PB), and whole ligament (WL). AB and PB have similar failure stress but different threshold strain, modulus, and failure strain. As a result, we recommend assigning different material properties (i.e., modulus and failure strain) to the two fiber bundles to realize biofidelic ligament responses in human body models. However, it is often inconvenient to perform tensile tests on AB and PB. Hence, we proposed a microstructural model-based approach to predict the material properties of AB and PB from the test results of WL. Such obtained modulus values of AB and PB had an error of 2% and 0.3%, respectively, compared with those measured from the tests. This approach can reduce the experimental cost for acquiring the needed mechanical property data for simulations.

Brain Material Properties and Integration of Arachnoid Complex for Biofidelic Impact Response for Human Head Finite Element Model.

Finite element head models offer great potential to study brain-related injuries; however, at present may be limited by geometric and material property simplifications required for continuum-level human body models. Specifically, the mechanical properties of the brain tissues are often represented with simplified linear viscoelastic models, or the material properties have been optimized to specific impact cases. In addition, anatomical structures such as the arachnoid complex have been omitted or implemented in a simple lumped manner. Recent material test data for four brain regions at three strain rates in three modes of loading (tension, compression, and shear) was used to fit material parameters for a hyper-viscoelastic constitutive model. The material model was implemented in a contemporary detailed head finite element model. A detailed representation of the arachnoid trabeculae was implemented with mechanical properties based on experimental data. The enhanced head model was assessed by re-creating 11 ex vivo head impact scenarios and comparing the simulation results with experimental data. The hyper-viscoelastic model faithfully captured mechanical properties of the brain tissue in three modes of loading and multiple strain rates. The enhanced head model showed a high level of biofidelity in all re-created impacts in part due to the improved brain-skull interface associated with implementation of the arachnoid trabeculae. The enhanced head model provides an improved predictive capability with material properties based on tissue level data and is positioned to investigate head injury and tissue damage in the future.

Predicting occupant head displacements in evasive maneuvers; tuning and comparison of a rotational based and a translational based neck muscle controller.

Objective: Real-life car crashes are often preceded by an evasive maneuver, which can alter the occupant posture and muscle state. To simulate the occupant response in such maneuvers, human body models (HBMs) with active muscles have been developed. The aim of this study was to implement an omni-directional rotational head-neck muscle controller in the SAFER HBM and compare the bio-fidelity of the HBM with a rotational controller to the HBM with a translational controller, in simulations of evasive maneuvers. Methods: The rotational controller was developed using an axis-angle representation of head rotations, with x, y, and z components in the axis. Muscle load sharing was based on rotational direction in the simulation and muscle activity recorded in three volunteer experiments in these directions. The gains of the rotational and translational controller were tuned to minimize differences between translational and rotational head displacements of the HBM and volunteers in braking and lane change maneuvers using multi-objective optimizations. Bio-fidelity of the model with tuned controllers was evaluated objectively using CORrelation and Analysis (CORA). Results: The results indicated comparable performance for both controllers after tuning, with somewhat higher bio-fidelity for rotational kinematics with the translational controller. After tuning, good or excellent bio-fidelity was indicated for both controllers in the loading direction (forward in braking, and lateral in lane change), with CORA scores of 0.86-0.99 and 0.93-0.98 for the rotational and translational controllers, respectively. For rotational displacements, and translational displacements in the other directions, bio-fidelity ranged from poor to excellent, with slightly higher average CORA scores for the HBM with the translational controller in both braking and lane changing. Time-averaged muscle activity was within one standard deviation of time-averaged muscle activity from volunteers. Conclusion: Overall, the results show that when tuned, both the translational and rotational controllers can be used to predict the occupant response to an evasive maneuver, allowing for the inclusion of evasive maneuvers prior to a crash in evaluation of vehicle safety. The rotational controller shows potential in controlling omni-directional head displacements, but the translational controller outperformed the rotational controller. Thus, for now, the recommendation is to use the translational controller with tuned gains.

A novel low trigger voltage low leakage SCR for low-voltage ESD protection

Reducing trigger voltage has always been a research hotspot in low-voltage electrostatic discharge (ESD) protection applications for integrated circuit. Thus, a novel low trigger voltage low leakage silicon-controlled rectifier (LTVLLSCR) for low-voltage ESD protection has been proposed. The proposed device uses a PMOS connected with the SCR to reduce the trigger voltage and the PMOS gate can be applied with the supply voltage to further reduce the trigger voltage and the leakage current. The operating principle and the physical mechanism of the proposed device were discussed by the Human Body Model simulation. The ESD characteristics of the proposed device were verified in 55 nm CMOS process. The experimental results demonstrate that the trigger voltage of the proposed device can reach a minimum of 2.86 V with an external bias, and the leakage current at 25 °C is about 1 nA which can be reduced by 13% with an external bias. With lower trigger voltage, lower leakage, smaller ESD design window and good ESD robustness, the LTVLLSCR is very suitable for 1 V low voltage applications.

Dual-directional SCR device with dual-gate controlled mechanism for ESD protection in photoelectric chip

The dual-directional silicon-controlled rectifier (DDSCR) is an electrostatic discharge (ESD) protection device. It can provide positive and negative ESD surge paths and has excellent robustness. However, industry-level sensors operating in strong electromagnetic interference environments impose higher reliability requirements on photoelectric chips. This paper proposed a novel DDSCR with a dual-gate controlled mechanism. By incorporating the gate diode triggering and the gate field modulation mechanism into the traditional DDSCR, and further utilizing additional parasitic bipolar junction transistors (BJTs) for diversion, the proposed device exhibits significantly improved ESD characteristics. Measurement results indicate that, compared to DDSCR, the proposed device exhibits a 27.5% reduction in trigger voltage (Vt1 ), a 96.1% improvement in holding voltage (Vh ), and achieves an equivalent human body model protection level of 11.45 kV, demonstrating exceptional design area efficiency. The experimental findings validate the effectiveness of the proposed device in 5 V photoelectric chip applications.

Population trends in human rib cross-sectional shapes.

Rib fractures remain the most frequent thoracic injury in motor vehicle crashes. Computational human body models (HBMs) can be used to simulate these injuries and design mitigation strategies, but they require adequately detailed geometry to replicate such fractures. Due to a lack of rib cross-sectional shape data availability, most commercial HBMs use highly simplified rib sections extracted from a single individual during original HBM development. This study provides human rib shape data collected from chest CT scans of 240 females and males across the full adult age range. A cortical bone mapping algorithm extracted cross-sectional geometry from scans in terms of local periosteal position with respect to the central rib axis and local cortex thickness. Principal component analysis was used to reduce the dimensionality of these cross-sectional shape data. Linear regression found significant associations between principal component scores and subject demographics (sex, age, height, and weight) at all rib levels, and predicted scores were used to explore the expected rib cross-sectional shapes across a wide range of subject demographics. The resulting detailed rib cross-sectional shapes were quantified in terms of their total cross-sectional area and their cortical bone cross-sectional area. Average-sized female ribs were smaller in total cross-sectional area than average-sized male ribs by between 20% and 36% across the rib cage, with the greatest differences seen in the central portions of rib 6. This trend persisted although to smaller differences of 14%-29% when comparing females and males of equal intermediate weight and stature. Cortical bone cross-sectional areas were up to 18% smaller in females than males of equivalent height and weight but also reached parity in certain regions of the rib cage. Increased age from 25 to 80 years was associated with reductions in cortical bone cross-sectional area (up to 37% in females and 26% in males at mid-rib levels). Total cross-sectional area was also seen to reduce with age in females but to a lesser degree (of up to 17% in mid-rib regions). Similar regions saw marginal increases in total cross-sectional area for male ribs, indicating age affects rib cortex thickness moreso than overall rib cross-sectional size. Increased subject height was associated with increased rib total and cortical bone cross-sectional areas by approximately 25% and 15% increases, respectively, in mid-rib sections for a given 30 cm increase in height, although the magnitudes of these associations varied by sex and rib location. Increased weight was associated with approximately equal changes in both cortical bone and total cross-sectional areas in males. These effects were most prominent (around 25% increases for an addition of 50 kg) toward lower ribs in the rib cage and had only modest effects (less than 12% change) in ribs 2-4. Females saw greater increases with weight in total rib area compared to cortical bone area, of up to 21% at the eighth rib level. Results from this study show the expected shapes of rib cross-sections across the adult rib cage and across a broad range of demographics. This detailed geometry can be used to produce accurate rib models representing widely varying populations.

Passenger muscle responses in emergency braking events with reclined seating

Emergency braking can generate forward displacement that may influence the effectiveness of protection in collisions, especially for passengers. The development of automated vehicles has enabled the diversification and rationalization of sitting positions, including reclined seating. However, the passenger response in pre-crash scenarios in reclined seating differs from that in standard seating, which poses different requirements for biofidelic human body models (HBMs) to evaluate passenger injuries in collisions. This study conducted emergency braking trials in vehicles at an initial velocity of 80 km/h. Five volunteers were exposed to approximately 1 g manual emergency braking (MEB), and the muscle responses at the front passenger seat with backrest angles of 25°, 45°, and 65° were recorded. The electromyography obtained from 14 muscles of the neck, torso, and lower extremity were normalized using maximum voluntary contractions (MVCs). In the quiet sitting phase, the activity levels were low (< 5% MVC) in all muscles for the three sitting positions. During emergency braking, the muscles are activated to restrict the body motion. There were differences in muscle amplitude and onset time in different backrest angles, with higher muscle activity levels in most muscles in a reclined sitting position. In particular, the sternocleidomastoid, rectus abdominis, and vastus medialis showed different patterns in the peak and steady-state phases. We found that the tibialis anterior was consistently activated at a lower level in all sitting postures (< 8% MVC), which indicates limited support of the shank for the body. The data provided in the paper are presented in corridors and intended for use in the development and validation of HBMs with active muscle models to simulate evasive maneuvers that potentially occur before a crash in the reclined sitting position.

Parametric Linear Blend Skinning Model for Multiple-Shape 3D Garments.

We present a novel data-driven Parametric Linear Blend Skinning (PLBS) model meticulously crafted for generalized 3D garment dressing and animation. Previous data-driven methods are impeded by certain challenges including overreliance on human body modeling and limited adaptability across different garment shapes. Our method resolves these challenges via two goals: 1) Develop a model based on garment modeling rather than human body modeling. 2) Separately construct low-dimensional sub-spaces for modeling in-plane deformation (such as variation in garment shape and size) and out-of-plane deformation (such as deformation due to varied body size and motion). Therefore, we formulate garment deformation as a PLBS model controlled by canonical 3D garment mesh, vertex-based skinning weights and associated local patch transformation. Unlike traditional LBS models specialized for individual objects, PLBS model is capable of uniformly expressing varied garments and bodies, the in-plane deformation is encoded on the canonical 3D garment and the out-of-plane deformation is controlled by the local patch transformation. Besides, we propose novel 3D garment registration and skinning weight decomposition strategies to obtain adequate data to build PLBS model under different garment categories. Furthermore, we employ dynamic fine-tuning to complement high-frequency signals missing from LBS for unseen testing data. Experiments illustrate that our method is capable of modeling dynamics for loose-fitting garments, outperforming previous data-driven modeling methods using different sub-space modeling strategies. We showcase that our method can factorize and be generalized for varied body sizes, garment shapes, garment sizes and human motions under different garment categories.

3D Animation Production and Generation Based on Human Body Base 3D Models

Abstract With the gradual increase in the demand for parametric human 3D models, the requirements for the accuracy of modeling and the quality of generation are getting higher and higher. In this paper, the human body structure model is constructed by 3D human posture estimation and spatio-temporal graph convolutional network, and after normalizing the data of the human body base, the 3D human body model based on the human body base is generated by combining the free deformation method, and the local light model is used to make the human body model more three-dimensional and real. In addition, the quality of 3D animation production is investigated by combining blind quality evaluation and validation analysis of the human body-based 3D model. The 3D human body modeling simulation error assessment has an error assessment root-means-square error that falls within 1.5%. In the validation analysis of model generation quality, the quality evaluation model proposed in this paper has a quality correlation coefficient and prediction accuracy of 0.9456 and 0.9454 on the dataset, respectively, and the performance of the ablation experiments and generalization experiments is stable. The mean value of the error between the animation production character generation effect and the actual testers is not more than 10mm.Combined with the research in this paper, it effectively improves the accuracy of human body modeling and provides an evaluation method for the quality of human 3D animation production and generation, which is of practical significance for its development.