## 4 Results and Discussion  
An evaluation of the fitting error was made, in order to determine the reconstruction quality between the output mesh and the input scan. The fitting error was defined as distance from a given vertex of the input scan to the nearest vertex in the reconstructed mesh. For each frame of the analyzed sequence, the reconstruction error for all vertices in the scan was computed (Table 1, full listing included in Appendix A) and then, based on that data, the average and median error for each frame was also calculated. Furthermore, in order to compare the results of our method with a state-of-the-art reference method, we carried out the analogous procedure for the reconstructed meshes shared by the authors of the Skinned Multi-Person Linear (SMPL) model method. In Figures 13 and 14, we present the  
Figure 11: From left to right: template mesh segment for torso, template after registration, and probed shape map. The map was sampled with resolution \(650\times 600\) in parameter space. Figure 12: Reconstructions of several frames chosen from the “jumping jacks” sequence by our method. average and median reconstruction errors for both methods in subsequent frames of testing sequence. Figure 15 depicts reconstruction error histograms for both methods in chosen frames of both sequences. Additionally, illustrations allowing for visual assessment of the reconstruction quality in chosen frames were prepared (compare Figures 12 and 16a). Figure 16b shows influence of the template mesh resolution on result reconstruction. On the grounds of the obtained error statistics, it can be seen that the proposed method features a better reconstruction quality than the reference method. The reconstruction errors of the proposed method were fundamentally lower than those in the case of SMPL, whereas the error distribution was shifted towards smaller values, indicating better reconstruction in a wider part of the mesh. The course of the average error in the \"jumping jacks\" sequence increased for frames 7065-7090 (Figure 13), where the motion was significantly faster and the arms were raised almost vertically (compare to Figure 1), increasing reconstruction difficulty in terms of arm, shoulder, and head occlusions. The median error did not change much, confirming the local character of increase of the reconstruction error in the mentioned frames. Figure 17 shows that our algorithm performs well with different or non-ideal skinning weights. The weaknesses of the proposed method include the uneven distribution of the resulting vertices in the end areas of the parametric mapping for given segments (Figure 18a). The reason for this lies in the mapping function, where the co-ordinates thicken around the poles, thus bringing more vertices into a similar area on the shape map. Moreover, in some frames, the output mesh was distorted due to data loss in the shape map (Figure 18b). Reconstruction based on such an erroneous map introduced artifacts in the resulting mesh. This problem was minimized by including a small fragment of adjacent segments into the shape map calculation; however, this solution is limited by the ability of the parametric mapping to map complex geometry into the 2D parametric space, without overlapping. Regarding pose tracking, one must admit, that the usage of the ICP method involves a limitation in robustness to input measurement frequency. In the presence of input data captured at a much smaller frequency than 60 fps, along with fast movement of the scanned person, the pose tracking  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frame No.