## 4 Experiments
### Results  
First, to determine the necessity of the human segmentation process, a paired \(t\)-test was performed on the actual measured body size values and the body size values inferred through the HMR-based technique without the human segmentation process. Table 7 describes the difference between the actual body measurements (direct measurement) and the body measurements based on the 3D model created from the original 2D image without removing the background under the HMR-based technique (i.e., no segmentation). In the case of body size (i.e., stature), which can be confirmed relatively accurately even in 2D images, accurate values were estimated, but for other body parts that included volume information, differences of as little as 0.90% (arm length) and as much as 4.30% (thigh \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gender** & **Age** & **Number of Subjects** & **Ratio (\%)** \\ \hline \multirow{8}{*}{Male} & 20–29 & 42 & 24 \\ \cline{2-4}  & 30–39 & 5 & 3 \\ \cline{2-4}  & 40–49 & 10 & 6 \\ \cline{2-4}  & 50–59 & 15 & 9 \\ \cline{2-4}  & 60–69 & 13 & 7 \\ \cline{2-4}  & 70– & 8 & 4 \\ \hline \multirow{8}{*}{Female} & 20–29 & 31 & 18 \\ \cline{2-4}  & 30–39 & 3 & 2 \\ \cline{2-4}  & 40–49 & 13 & 7 \\ \cline{1-1} \cline{2-4}  & 50–59 & 19 & 11 \\ \cline{1-1} \cline{2-4}  & 60–69 & 12 & 7 \\ \cline{1-1} \cline{2-4}  & 70– & 3 & 2 \\ \hline Total & & 174 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of subject demographic characteristics. circumference) were observed. The reason for this result is that during the process of creating a 3D model, the size parameters of body parts that require volume information, such as thighs or arms, are predicted relatively inaccurately because they are inferred together with the surrounding background. When a paired _t_-test is conducted between the two values in Table 7, the _p_-value of the paired _t_-test at \(\alpha\) = 0.05 is 0.0268, which means that there is significant difference between the two measurement methods. This result confirms that the HMR-based technique without the human segmentation process is an inadequate methodology for predicting body dimensions by generating 2D images into 3D images. In the semantic segmentation combined HMR frameworks, we can consider which pretrained model for human segmentation will be selected for the rapid measurement process. The considered pretrained models were (1) the Xception architecture [40] using MS\(-\)COCO [41] and the PASCAL VOC training\(-\)validation dataset, (2) Xception architecture using MS-COCO and the PASCAL VOC training\(-\)augmented dataset, (3) Mobilenetv2 [55] architecture using MS\(-\)COCO and the PASCAL VOC training\(-\)validation dataset, and (4) Mobilenetv2 architecture using MS-COCO and the PASCAL VOC training\(-\)augmented dataset. As mentioned in Table 7, the prediction accuracy of body dimensions that can be identified even in 2D images, such as stature, showed that accurate predictions were possible in all methodologies. However, in body parts requiring information on volume, differences were observed depending on the methodology, with the highest difference of 3.02% on average observed in neck circumference, excluding stature, and the lowest difference of 0.52% observed in arm length. According to the measured results described in Table 8, the _p_-values of the paired _t_-tests at \(\alpha\) = 0.05 are 0.0682, 0.09, 0.3483, and 0.3434, respectively, which means that the measurement technique using pretrained models (1), (2), (3), and (4) exhibits no significant difference to the direct measurement approach. However, considering that the shape of the body can vary according to each anthropometric variable, the human segmentation process based on conditions (3) and (4) is optimal to modeling the body model through the image-based reconstruction process. Figure 5 shows examples of segmented images for each condition.