3D Body Model Research Articles

A Human Body Simulation Using Semantic Segmentation and Image-Based Reconstruction Techniques for Personalized Healthcare

The global healthcare market is expanding, with a particular focus on personalized care for individuals who are unable to leave their homes due to the COVID-19 pandemic. However, the implementation of personalized care is challenging due to the need for additional devices, such as smartwatches and wearable trackers. This study aims to develop a human body simulation that predicts and visualizes an individual’s 3D body changes based on 2D images taken by a portable device. The simulation proposed in this study uses semantic segmentation and image-based reconstruction techniques to preprocess 2D images and construct 3D body models. It also considers the user’s exercise plan to enable the visualization of 3D body changes. The proposed simulation was developed based on human-in-the-loop experimental results and literature data. The experiment shows that there is no statistical difference between the simulated body and actual anthropometric measurement with a p-value of 0.3483 in the paired t-test. The proposed simulation provides an accurate and efficient estimation of the human body in a 3D environment, without the need for expensive equipment such as a 3D scanner or scanning uniform, unlike the existing anthropometry approach. This can promote preventive treatment for individuals who lack access to healthcare.

Hop Distance Symmetry Moderately Reflects Knee Biomechanics Symmetry During Landing But Not For Controlled Propulsions.

Landing with poor knee sagittal plane biomechanics has been identified as a risk factor for Anterior Cruciate Ligament (ACL) injury. However, it is unclear if the horizontal hop test battery reflects knee function and biomechanics. To investigate the correlation between clinical limb symmetry index (LSI) and landing and propulsion knee biomechanics during the hop test battery using markerless motion capture. Cross-sectional biomechanics laboratory study. Forty-two participants with and without knee surgery (age 28.0 ± 8.0 years) performed the hop test battery which consisted of a single hop for distance, crossover hop, triple hop, and 6-m timed hop in the order listed. Eight high speed cameras were used to collect simultaneous 3D motion data and Theia 3D (Theia Markerless Inc.) was used to generate 3D body model files. Lower limb joint kinematics were calculated in Visual3D. Correlation (Spearman's ρ) was computed between clinical LSI and symmetry in peak and initial contact (IC) knee flexion angle during propulsion and landing phases of each movement. In the single hop, clinical LSI showed positive correlation with kinematic LSI at peak landing (ρ= 0.39, p=0.011), but no correlation at peak propulsion (ρ= -0.03, p=0.851). In the crossover hop, non-significant correlations were found in both propulsion and landing. In the triple hop, positive correlation was found at peak propulsion (ρ= 0.38, p=0.027), peak landing (ρ= 0.48 - 0.66, p<0.001), and last landing IC (ρ= 0.45, p=0.009). In the timed hop, peak propulsion showed positive correlation (ρ= 0.51, p=0.003). Single hop and triple hop distance symmetry reflected landing biomechanical symmetry better than propulsion symmetry. Poor scores on the hop test battery reflect asymmetrical knee landing biomechanics, emphasizing the importance of continuing to use the hop test battery as part of clinical decision making. 3b.

An intelligent recommendation system for personalised parametric garment patterns by integrating designer’s knowledge and 3D body measurements

Garment pattern-making is one of the most important parts of the apparel industry. However, traditional pattern-making is an experience-based work, very time-consuming and ignores the body shape difference. This paper proposes a parametric design method for garment pattern based on body dimensions acquired from a body scanner and body features (body feature points and three segmented body part shape classification) identified by designers according to their professional knowledge. By using this method, we construct a men’s shirt pattern recommendation system oriented to personalised fit. The system consists of two databases and three models. The two databases include a relational database (Database I) and a personalised basic pattern (PBP) database (Database II). The Database I is based on manual and three-dimensional (3D) measurements of human bodies by using designer’s knowledge. And Database I is a relational database, which is organised in terms of the relational model of the body part shape and its key body feature dimensions. After a deep analysis of measured data, the irrelevant measured dimensions to human body shape have been excluded by designers and extract representative human body feature dimensions. In addition, the relations between body shapes and previously identified body feature dimensions have been modelled. From the above relational model, we label key feature point positions on the corresponding 3D body model obtained from 3D body scanning and correct the whole 3D human upper body model into the semantically interpretable one. The 3D personalised basic pattern is drawn on the corrected model based on these key feature points. By using three-dimensional to two-dimensional (3D-to-2D) flattening technology, a 2D flatten graph of the 3D personalised basic pattern of the interpretable model is obtained and slightly adjusted to the form suitable for industrial production, i.e., PBP and the PBP database (Database II) is built. In addition, the three models include a basic pattern parametric model (Model I) (characterizing the relations between the basic pattern and its key influencing human dimensions (chest girth and back length)), a regression model (Model II) which enables to infer from basic pattern to PBP for three body parts based on the one-to-one correspondence of key points between the PBPs and the basic patterns and a personalised shirt pattern parametric model (Model III) (characterizing the structural relations between the personalised shirt pattern (PBPshirt) and PBP). The initial input items of the recommendation system are the body dimension constraint parameters, including chest girth, back length and the body feature dimensions used to determine each body part shape as well as three shirt style constraint parameters (slim, regular and loose). By using Model I, the corresponding basic pattern can be generated through the user’s chest girth and back length. Body feature dimensions determine the three body parts’ shapes. Then, Model II is used to generate the PBP for the corresponding body parts shape. Based on the shirt style chosen by the user, Mode III is used to generate the PBPshirt from the PBP. The output of the recommendation system is a fit-oriented PBPshirt. Moreover, if the PBPshirt is unsatisfactory after a virtual try-on, four adjustable parameters (front side-seam dart, back side-seam dart, waist dart and garment bodice length) are designed to adjust the PBPshirt generated by the proposed recommendation system.

Status of research on parametric methods for the reconstruction of 3D models of the human body for virtual fitting

PurposeThis paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.Design/methodology/approachIn this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.FindingsConsidering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.Originality/valueThis article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

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 (&gt;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

Enhancing 3D human pose estimation with NIR single-pixel imaging and time-of-flight technology: a deep learning approach.

The extraction of 3D human pose and body shape details from a single monocular image is a significant challenge in computer vision. Traditional methods use RGB images, but these are constrained by varying lighting and occlusions. However, cutting-edge developments in imaging technologies have introduced new techniques such as single-pixel imaging (SPI) that can surmount these hurdles. In the near-infrared spectrum, SPI demonstrates impressive capabilities in capturing a 3D human pose. This wavelength can penetrate clothing and is less influenced by lighting variations than visible light, thus providing a reliable means to accurately capture body shape and pose data, even in difficult settings. In this work, we explore the use of an SPI camera operating in the NIR with time-of-flight (TOF) at bands 850-1550nm as a solution to detect humans in nighttime environments. The proposed system uses the vision transformers (ViT) model to detect and extract the characteristic features of humans for integration over a 3D body model SMPL-X through 3D body shape regression using deep learning. To evaluate the efficacy of NIR-SPI 3D image reconstruction, we constructed a laboratory scenario that simulates nighttime conditions, enabling us to test the feasibility of employing NIR-SPI as a vision sensor in outdoor environments. By assessing the results obtained from this setup, we aim to demonstrate the potential of NIR-SPI as an effective tool to detect humans in nighttime scenarios and capture their accurate 3D body pose and shape.

Development of a Non-Contact Sensor System for Converting 2D Images into 3D Body Data: A Deep Learning Approach to Monitor Obesity and Body Shape in Individuals in Their 20s and 30s.

This study demonstrates how to generate a three-dimensional (3D) body model through a small number of images and derive body values similar to the actual values using generated 3D body data. In this study, a 3D body model that can be used for body type diagnosis was developed using two full-body pictures of the front and side taken with a mobile phone. For data training, 400 3D body datasets (male: 200, female: 200) provided by Size Korea were used, and four models, i.e., 3D recurrent reconstruction neural network, point cloud generative adversarial network, skinned multi-person linear model, and pixel-aligned impact function for high-resolution 3D human digitization, were used. The models proposed in this study were analyzed and compared. A total of 10 men and women were analyzed, and their corresponding 3D models were verified by comparing 3D body data derived from 2D image inputs with those obtained using a body scanner. The model was verified through the difference between 3D data derived from the 2D image and those derived using an actual body scanner. Unlike the 3D generation models that could not be used to derive the body values in this study, the proposed model was successfully used to derive various body values, indicating that this model can be implemented to identify various body types and monitor obesity in the future.

From prediction to measurement, an efficient method for digital human model obtainment

Digital human has been increasingly used in industry, for example in Metaverse which has been a popular topic in recent years. The existing method of obtaining digital human models are either expensive or lack of accuracy. In this paper, we discuss a novel method to reconstruct a 3D human model from 2D images captured by a monocular camera. The input of our method only requires a set of rotated human body images that can accept slight movement. First, we apply a deep learning method to predict an initial 3D human body model from multi-view human body images. Then the total detailed digital human model will be computed and optimized. The typical method requires the human body and cameras fixed to obtain a visual hull from a significant number of camera images. This could be extremely expensive and inconvenient when such an application is developed for online users. Compared to the structural lighting measurement system, our predict-optimized framework only requires several input images captured by personal equipment to provide enough accuracy and online use resolution results.

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.

Exploring the Application of Computer Visual Arts in the Context of Digital Media

Abstract This paper provides an in-depth discussion on the critical technologies of computer visual arts in digital media, mainly focusing on virtual character modeling and human body tracking technologies. The study merges the models into the same coordinate system by combining point cloud data from different angles and calculating the transformation matrix. Accurate 3D human body models were reconstructed using point cloud alignment and fusion techniques. A particle filtering method combined color, motion and shape information for effective tracking. The experimental results show that the success rate of hand tracking reaches 95.35% in the case of no background interference, while the success rate is 90.97% in the environment with background interference. In addition, the study shows that low resolution images (584×388) outperform high resolution (968×676) in terms of tracking accuracy and speed. The study confirms that the combination of advanced computer vision techniques can effectively construct and follow virtual character models and realize high-precision human motion capture under different environmental conditions.

Digital Inheritance of Traditional Mongolian Robes of the Nayman Tribe

Abstract The Mongolian robe is the crystallisation of the wisdom of Mongolian people after a long period of social practice and one of the intangible cultural heritages of humanity. With the gradual disappearance of the nomadic lifestyle in the steppe, it is not easy to protect and pass on traditional Mongolian dress culture and traditional handicraft technology. This study used the digital twin concept to construct a digital twin to try on clothes for a model in a virtual environment, to realise a mirror image presentation of the target physical entity, and to use effective modern technology to protect and pass on the traditional clothing. The experiment used an AlphaM4 3D body scanner to obtain a 3D human body model, as well as reverse engineering technology to process the initial data, such as noise reduction, streamlining, hole patching and smoothing to build a personalised human body model. The traditional black cotton robe of the Nayman tribe researched in the field was mapped by the planar structure design method, and its structure and 2D samples were recovered. A 3D model of the traditional cotton robe of the Nayman tribe was established based on 3D simulation technology to achieve a 3D virtual display effect, enable better dissemination and inheritance, and give a new vitality to non-heritage clothing in the digital era. The research results can provide fresh ideas and technical support for the digital estate and development of other traditional costumes, as well as contribute to the construction of digital resources for intangible cultural heritage.

Taxonomy and Survey of Current 3D Photorealistic Human Body Modelling and Reconstruction Techniques for Holographic-Type Communication

The continuous evolution of video technologies is now primarily focused on enhancing 3D video paradigms and consistently improving their quality, realism, and level of immersion. Both the research community and the industry work towards improving 3D content representation, compression, and transmission. Their collective efforts culminate in the striving for real-time transfer of volumetric data between distant locations, laying the foundation for holographic-type communication (HTC). However, to truly enable a realistic holographic experience, the 3D representation of the HTC participants must accurately convey the real individuals’ appearance, emotions, and interactions by creating authentic and animatable 3D human models. In this regard, our paper aims to examine the most recent and widely acknowledged works in the realm of 3D human body modelling and reconstruction. In addition, we provide insights into the datasets and the 3D parametric body models utilized by the examined approaches, along with the employed evaluation metrics. Our contribution involves organizing the examined techniques, making comparisons based on various criteria, and creating a taxonomy rooted in the nature of the input data. Furthermore, we discuss the assessed approaches concerning different indicators and HTC.

Design of a Novel Flexible Spherical Hinge and Its Application in Continuum Robot

Abstract Compliant mechanisms, which can be integrally machined and without assembly, are well suited as joints for continuum robots (CRs), but how to incorporate the advantages of the compliant mechanism into the arm design is a key issue in this work. In this paper, a novel type of flexible spherical-hinged (FSH) joint composed of tetrahedron elements with a fixed virtual remote center of motion (RCM) at the bottom is proposed, and then extended to the CR and end-effector. In the arm design, the error compensation principle is used to offset the parasitic motion of the CR under external load (pressure and torque) and improve the bending and torsional isotropy of the arm through different series combinations, and then the stiffness model of the FSH joint and the statics model of the CR are developed using the 3D chain pseudo-rigid body model (3D-CPRBM) and tested. The results show that the 3D-CPRBM can effectively predict the deformation of the FSH joint and the CR. Moreover, the maximum standard deviation of the bending angle of the FSH joint in each direction is only 0.26 deg, the repeatable positioning accuracy of the CR can reach 0.5 deg, and the end-effector has good gripping ability and self-adaptive capability.

3D human body modeling with orthogonal human mask image based on multi-channel Swin transformer architecture

The reconstruction based on RGB images of dressed human body lacks the shape information of the human body under clothing, while the naked 3D human body scanning will violate the user's privacy. To overcome these limitations, a new method, based on Swin transformer (Swin-T), for reconstructing 3D human body shape from human orthogonal mask image is proposed. Its core is to express the reconstruction problem as solving regression mapping function. A fast body shape type classification method based on the human front mask is proposed. The regression function is innovatively represented as a piecewise function, with the body shape of the human body as the segmentation criterion. A multi-channel Swin-T architecture is designed, which can not only extract features from front and side mask images, but also their mixed features to construct the regression mapping function. Different body types for different genders are predicted with separate regression function to help estimate an accurate human model. Extensive experimental results show that the proposed method effectively achieves visually realistic and accurate body reconstruction, and significantly outperforms the current state-of-the-art methods. In addition, the classification of body types can compensate for the errors caused by partial clothing laxity in practical applications, which is beneficial for users to obtain a more accurate 3D human model.

Single-view multi-human pose estimation by attentive cross-dimension matching.

Vision-based human pose estimation has been widely applied in tasks such as augmented reality, action recognition and human-machine interaction. Current approaches favor the keypoint detection-based paradigm, as it eases the learning by circumventing the highly non-linear problem of direct regressing keypoint coordinates. However, in such a paradigm, each keypoint is predicted based on its small surrounding region in a Gaussian-like heatmap, resulting in a huge waste of information from the rest regions and even limiting the model optimization. In this paper, we design a new k-block multi-person pose estimation architecture with a voting mechanism on the entire heatmap to simultaneously infer the key points and their uncertainties. To further improve the keypoint estimation, this architecture leverages the SMPL 3D human body model, and iteratively mines the information of human body structure to correct the pose estimation from a single image. By experiments on the 3DPW dataset, it improves the state-of-the-art performance by about 8 mm on MPJPE metric and 5 mm on PA-MPJPE metric. Furthermore, its capability to be employed in real-time provides potential applications for multi-person pose estimation to be conducted in complex scenarios.

A Novel Screening Method for Scoliosis Using A Bodysuit and 3-dimensional Imaging.

A single-center prospective observational study. To clarify the usefulness of a novel scoliosis screening method using a 3-dimensional (3D) human fitting application and a specific bodysuit. Several scoliosis screening methods, such as scoliometer and Moiré topography, are available for detecting scoliosis. In the present study, a novel screening method for scoliosis using a 3D human fitting application and a specific bodysuit was developed. Patients with scoliosis or suspected scoliosis, non-scoliosis patients, and healthy volunteers were enrolled. They were divided into Non-Scoliosis and Scoliosis groups. The Scoliosis group was further subdivided into Mild-, Moderate-, and Severe-Scoliosis. Patients' characteristics and Z values, which were calculated by a 3D virtual human body model created by a 3D human fitting application and a specific bodysuit to evaluate trunk asymmetry caused by scoliosis, were compared between the Non-Scoliosis and Scoliosis groups or among the Non-, Mild-, Moderate- and Severe-Scoliosis groups. Finally, the optimal cutoff of the Z value was determined to detect moderate to severe scoliosis using receiver operating characteristics (ROC) curve analysis. A total of 101 patients were included. The Non-Scoliosis group consisted of 47 patients, and the Scoliosis group included 54 patients, with 11, 31, and 12 patients in the Mild-, Moderate-, and Severe-Scoliosis groups, respectively. The Scoliosis group showed a significantly higher Z value than the Non-Scoliosis group. The Moderate- or Severe-Scoliosis group had a significantly higher Z value than the Non- or Mild-Scoliosis group. The ROC curve analysis revealed that the optimal cutoff of the Z value was 19.9mm (sensitivity, 95.3%; specificity, 58.6%). A novel scoliosis screening method consisting of a 3D human fitting application and a specific bodysuit may be useful for detecting moderate to severe scoliosis.

Markerless motion tracking to quantify behavioral changes during robot-assisted gait training: A validation study.

Introduction: Measuring kinematic behavior during robot-assisted gait therapy requires either laborious set up of a marker-based motion capture system or relies on the internal sensors of devices that may not cover all relevant degrees of freedom. This presents a major barrier for the adoption of kinematic measurements in the normal clinical schedule. However, to advance the field of robot-assisted therapy many insights could be gained from evaluating patient behavior during regular therapies. Methods: For this reason, we recently developed and validated a method for extracting kinematics from recordings of a low-cost RGB-D sensor, which relies on a virtual 3D body model to estimate the patient's body shape and pose in each frame. The present study aimed to evaluate the robustness of the method to the presence of a lower limb exoskeleton. 10 healthy children without gait impairment walked on a treadmill with and without wearing the exoskeleton to evaluate the estimated body shape, and 8 custom stickers were placed on the body to evaluate the accuracy of estimated poses. Results & Conclusion: We found that the shape is generally robust to wearing the exoskeleton, and systematic pose tracking errors were around 5mm. Therefore, the method can be a valuable measurement tool for the clinical evaluation, e.g., to measure compensatory movements of the trunk.

Compact Dual-Band On-Body Near Field Antenna With Reflector for Measuring Deep Core Temperature

Human deep core temperature is a vital health condition parameter. Human body temperature change begins from the deep core followed by change in skin temperature. Measuring deep core temperature is the first action for rapid detection of health condition. Infrared thermometer can measure temperature; however, the only measures the skin temperature. The use of microwave radiometer for measuring deep core temperature has garnered attention. The antenna, an important component of the microwave radiometer, has rarely been focused upon in studies on measurement of deep core temperature. This study proposed compact dual-band on-body near field antenna with a reflector for measuring deep core temperature using a microwave radiometer. The proposed antenna size is 13 mm × 13 mm with a 20 mm × 20 mm reflector 3 mm above the radiator. The proposed antenna was simulated and designed on a full 3D body model and simplified flat phantom and measured on real human wrist. It achieved sufficient bandwidth and volume loss density can be used in microwave radiometers for measuring deep core temperature.

Facial Animation with Disentangled Identity and Motion using Transformers

AbstractWe propose a 3D+time framework for modeling dynamic sequences of 3D facial shapes, representing realistic non‐rigid motion during a performance. Our work extends neural 3D morphable models by learning a motion manifold using a transformer architecture. More specifically, we derive a novel transformer‐based autoencoder that can model and synthesize 3D geometry sequences of arbitrary length. This transformer naturally determines frame‐to‐frame correlations required to represent the motion manifold, via the internal self‐attention mechanism. Furthermore, our method disentangles the constant facial identity from the time‐varying facial expressions in a performance, using two separate codes to represent neutral identity and the performance itself within separate latent subspaces. Thus, the model represents identity‐agnostic performances that can be paired with an arbitrary new identity code and fed through our new identity‐modulated performance decoder; the result is a sequence of 3D meshes for the performance with the desired identity and temporal length. We demonstrate how our disentangled motion model has natural applications in performance synthesis, performance retargeting, key‐frame interpolation and completion of missing data, performance denoising and retiming, and other potential applications that include full 3D body modeling.

Toward Clothing Personalized Customization Multi-Perspective Silhouettes 3D Human Body Rapid Reconstruction

The clothing personalized online customization is required to design around the user shape parameters, and the traditional anthropometry method obtains the user shape parameter with a low speed and a high error. To tackle this issue, a rapid 3D human body reconstruction method is proposed based on multi-perspective silhouettes for clothing personalized customization. The multilevel dilated convolution semantic network (MDS-Net) is leveraged to extract the global and local features in the human silhouettes to implement the semantic segmentation. The torso parameter extraction network (TPE-Net) is leveraged to extract the shape and pose parameters of the multi-perspective human body segmentation maps. The principal component analysis (PCA) is leveraged to extract the semantic features in the latent space of the 3D human body model, and the semantic features are mapped to the shape and posture parameters output by the torso parameter extraction network, thus, 3D human body model is reconstrued. This method was verified in a PyTorch environment with four datasets. The experiments demonstrated that the mIoU of the multilevel dilated convolution semantic network on the test set is 0.881, which can achieve overall segmentation and local detail preservation. The torso parameter extraction network has an accuracy of 0.74 in the shape parameter prediction on the test set, the prediction of joint offset distance is proportional to the index in the kinematic tree. The entire 3D human body reconstruction method is verified by the real cases.