## 2 State-of-the-Art Review  
State-of-the-art pose assessment applications now drive towards a 3D space, which provides quite an interesting research context. A human person can easily \"reconstruct\" a 3D shape from a 2D-only image. However, this poses a problem to \"classic\" or deep-learning processors, as the algorithm has to fit joints to a 3D skeleton [48]. Kinematic features of full 3D human pose representations are high-dimensional and difficult to estimate directly. The more poses a model aims to support, the higher the complexity increase in the distribution of poses, and the higher the dimensionality of the model [49]. Dynamics of motion is another problem often resulting in misalignment of the estimated mesh [50]. This overview offers insights into research on pure 2D image-based conversion, intentionally omitting depth-based approaches [51], as this was not the objective of this study and leads to the limitation of dedicated depth sensors. Human pose estimation itself can be defined as targeting an estimation of the position and/or spatial location of body key points in an image frame [52]. The result is often a pose composed of joints, depicting the structure of a human body [53]. Traditional (classic) methods often include the pictorial structure model targeting the analysis of a kinematic tree to find the main joints of a human body [54], using the histogram of the orientation gradient (HOG), the hue, saturation, and value (HSV) color model, and other methodsto acquire information about shape, color, and other parameters, which are later used to infer the human pose. Such simple but low-performing models are now replaced with those based on deep-learning approaches with the advantage of good robustness and the capability to learn pose characteristics from global space [55]. Still, no matter whether a classic or modern approach, the process of estimating human poses is similar, as first the algorithm must localize the joints of the human body and then group this information into a representation of a pose [56]. One of the most popular approaches is pose machines, which provide sequential prediction methods for learning spatial models [57]. Many convolutional neural network (CNN) approaches were modified to support more dimensions of skeleton keypoint matching [58] and then improved, for example, by applying spatial-temporal graph convolutional networks [59] to learn both spatial and temporal patterns from 2D images. Another approach [60] suggested using probabilistic knowledge of plausible 3D keypoint positions to fine-tune the search to estimate a 3D human pose with a multi-stage CNN architecture. [PERSON] et al. described a graph convolutional network (GCN) that works on regression tasks on graph-structured data, which can improve performance with negligible overhead [61]. [PERSON] et al. [62] suggested using dilated temporal convolutions on 2D keypoints in combination with backprojection for training to use unlabeled video data. [PERSON] et al. [63] showed that a 3D CNN could be used for voxel-to-voxel-like mapping and prediction to solve depth problems, using a 3D voxelized grid for estimation of per-voxel likelihoods for each joint. [PERSON] et al. suggested that using depth data to obtain 3D lifted points from 2D information can provide a rough estimate of the true 3D human pose, by separating the 2D pose estimation from the 3D pose refinement [64]. Volumetric representations might work well to achieve good pixel-level localization accuracy, but unfortunately often lead to unrealistic body structures as a result. This can be solved by linking body mesh estimation and 3D keypoint estimation [65]. Most of the above CNNs rely mostly on scale-invariant, translation-invariant, or rotation-invariant operations, such as max-pooling, and have a problem with viewpoint generalization, which can be solved by autoencoder architectures [66].