## 2 Three-Dimensional Modeling of the Human Body  
As the demand for 3D human body modeling increases in multiple industries (e.g., clothing, healthcare, gaming, and industrial design), researchers are intensively conducting research in the field of human body shape modeling (HBSM). According to [PERSON] and [PERSON] [26], the HBSM techniques can be categorized as follows: (1) Direct model creation/acquisition modeling of the 3D human body via depth-related information or a map of per-pixel data, e.g., RGB\(-\)D data; (2) template-based model scaling, which modifies template models to elicit the desired 3D human body model; (3) statistical-based model synthesis, deforming pose and shape by learning datasets; and (4) image-based reconstruction, extracting parameters that elicit the 3D human body model from 2D images. The direct model creation/acquisition technique is the modeling of a 3D model via surface spatial data obtained by a 3D scanner, which is based on photogrammetry, structured light, and laser scanning techniques. In photogrammetry, multiple lenses are used to obtain the spatial data of an object. Once two charge-coupled devices (CCDs) located at a parallax angle take two different object images, the technique calculates the distance between a base plane and points on the object by using their geometrical relationship [27]. The laser scanning technique with triangulation [28] and the structured light-based technique using contour mapping (e.g., moire topography) are also widely used techniques due to their modeling accuracy and cost efficiency [29]. Template-based model scaling generates a 3D model by scaling a body part categorized by the deformation of applied types: (1) vertical deformation part, such as waist height, and inside leg length, (2) joint girth (circumference) deformation, such as knee girth and wrist girth, and (3) girth deformation between two joints or regions, such as thigh girth and waist girth [30]. The major advantage of template-based model scaling compared to the other techniques is its effectiveness, because it generates a body model from a template, while expensive equipment (e.g., a 3D scanner) is unnecessary. Statistical-based model synthesis was devised to overcome the inefficiency of the direct model creation/acquisition technique in terms of cost and labor. Instead of observing and measuring all body parts every time, this technique models a homogeneous face model database with principal component analysis (PCA) in analyzing shape variation [31]. Once the database is constructed, the dominant variables that determine the 3D model face are analyzed and extracted. By generating these variables, the objective 3D body is modeled. In this technique, Shape Completion and Animation of PEople (SCAPE) and Skinned Multi-Person Linear (SMPL) techniques are widely used to understand shape variations under different body shapes [32; 33]. Image-based reconstruction focuses on modeling a 3D body model from an existing 2D image. Since it does not require the numerous image collection efforts necessary for the other three techniques, it is known as the most cost-effective technique. One of the popular models is DeepCut [34], using a convolutional neural network (CNN) to reconstruct the posture of a human body via partitioning and the detection of body parts. [PERSON] et al. [35] proposed an advanced framework called SMPLify based on CNN and SMPL. In this technique, SMPLify identifies 2D body joint locations using DeepCut, and the derived 2D body joints are used to calibrate the pose (\(\theta\)) and shape (\(\beta\)) parameters of the SMPL model. Furthermore, to fit the pose and shape to 2D body joints, a minimizing objective function is used that represents the sum of joint-based data terms, \(E_{j}(\beta,\theta;K,J_{est})\), pose priors, \(E_{a}(\theta)\)and \(E_{\theta}(\theta)\), a shape prior, \(E_{\beta}(\beta)\), and a penalty term, \(E_{sp}(\theta;\beta)\).