## 4 Experiments
### Results
In Figure 5, there is a minor difference between the actual body dimensions and the dimension values from the inferred 3D image because the background images of the armpits and between the legs, where the gap between the body and the clothes is relatively narrow, were not accurately removed. Although these aspects need improvement, we can still confirm that more accurate body dimension prediction is possible through the proposed segmentation process than the existing HMR approach. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Anthropometric Variable** & **Direct Measurement** & **HMR without Segmentation** \\ \hline \hline
**Stature (cm)** & 169.09 & 169.09 \\ \hline
**Waist Circumference** & 83.41 & 84.90 \\ (Natural Indentation; cm) & 76.90 & 74.23 \\ (Omphalion; cm) & 96.94 & 93.95 \\ \hline
**Wrist Circumference (cm)** & 15.27 & 15.11 \\ \hline
**Neck Circumference (cm)** & 43.98 & 42.12 \\ \hline
**Arm Length (cm)** & 51.20 & 50.74 \\ \hline
**Thigh Circumference (cm)** & 57.01 & 54.56 \\ \hline
**Biacromial Breadth (cm)** & 48.52 & 46.95 \\ \hline
**Hip Circumference (cm)** & 96.94 & 95.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Anthropometric variables of the direct measurement approach and the HMR-based technique without segmentation. In addition, we conducted computational complexity experiments on Xception architecture and Mobilenetv2 architecture, which are the main methodologies used in semantic  
Figure 5: Examples of human segmentation images based on the conditions of the pretrained model: (**a**) original image; (**b**) segmented image with Xception architecture trained with coco_voctrianval dataset; (**c**) segmented image with Xception architecture trained with coco_voctrainaug dataset; (**d**) segmented image with mobilenetv2 architecture trained with coco_voctrainval dataset; (**e**) segmented image with mobilenetv2 architecture trained with coco_voctrainaug dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Anthropometric Variable** & **Direct Measurement** & **Condition (1)** & **Condition (2)** & **Condition (3)** & **Condition (4)** \\ \hline \begin{tabular}{c} Stature (cm) \\ \end{tabular} & 169.09 & 169.09 & 169.09 & 169.09 & 169.09 \\ \hline \begin{tabular}{c} Waist circumference \\ (Natural indentation; cm) \\ \end{tabular} & 83.41 & 82.81 & 82.9 & 82.04 & 82.01 \\ \hline \begin{tabular}{c} Waist circumference \\ (Omphalion; cm) \\ \end{tabular} & 76.90 & 78.38 & 78.23 & 77.77 & 77.77 \\ \hline \begin{tabular}{c} Bust circumference (cm) \\ \end{tabular} & 96.94 & 94.89 & 93.95 & 98.06 & 97.14 \\ \hline \begin{tabular}{c} Wrist circumference (cm) \\ \end{tabular} & 15.27 & 15.07 & 15.11 & 15.12 & 15.21 \\ \hline \begin{tabular}{c} Neck circumference (cm) \\ \end{tabular} & 43.98 & 42.03 & 42.12 & 43.14 & 43.32 \\ \hline \begin{tabular}{c} Arm length (cm) \\ \end{tabular} & 51.20 & 50.84 & 50.74 & 51.1 & 51.05 \\ \hline \begin{tabular}{c} Thigh circumference (cm) \\ \end{tabular} & 57.01 & 54.88 & 54.56 & 55.78 & 56.69 \\ \hline \
\begin{tabular}{c} Biaromial breadth (cm) \\ \end{tabular} & 48.52 & 47.98 & 47.95 & 48.75 & 48.57 \\ \hline
\begin{tabular}{c} Hip circumference (cm) \\ \end{tabular} & 96.94 & 96.01 & 96.86 & 95.62 & 95.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Anthropometric variables of the direct measurement approach, and each condition. segmentation. In the process of converting 513 \(\times\) 513 image inputs into 3D models, Xception architecture was applied to conditions (1) and (2), and Mobilenetv2 architecture was applied to conditions (3) and (4). The results of measuring the FLOPs (Floating Point OPerations), number of parameters (#param. ), and GPU utilization (%) for the two architectures are described in Table 9. The experiment was conducted on the Dell Precision Workstation (Intel Xeon @ Silver 4210 Processor, Nvidia RTX Quadro 4000, and Ubuntu 20.04), Austin, TX, USA.