# Pose-Driven Body Shape Prediction Algorithm Based on the Conditional GAN
## 4 Results  
Samples of the findings of a qualitative comparison of the base and proposed models based on the front and side datasets are shown in Figures 9 and 10. Pix2 Body successfully infers body shape despite the presence of occluded parts that conceal the body shape, such as a skirt that covers the entire lower body or a shirt that is significantly larger than the upper body size, as illustrated in Figure 9. In the case of the baseline model, the results on the side dataset, in contrast to those on the front dataset, clearly show missing regions, particularly in the arms and legs. By contrast, Pix2 Body is able to reconstruct regions that are difficult to infer in both the front and side parts of the body, such as body bends and unstructured arms and legs. The model also successfully generates the body shape even in dynamic postures; this is because we included datasets for both static and dynamic postures in the training. Pix2 Body significantly outperforms the baseline model (Pix2 Pix). Table 2 presents a summary of the SSIM scores and visual comparisons between the baseline and the proposed models under both front and side views. The structural similarity index measure (SSIM) quantitatively evaluates the perceptual similarity between the predicted and ground-truth images by considering luminance, contrast, and structural information. Higher SSIM scores indicate better reconstruction quality. It has also been determined that including body information, such as foot orientation and upper body size, using body joint points allows for a more accurate body shape to be generated. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**View** & **Input** & **Occlusion** & **Baseline** & **Our** & **Key Differences** \\
**Direction** & **Condition** & **Characteristics** & **Model,** & **SSIM** & **(Observed Findings)** \\ \hline \multirow{4}{*}{Front} & \multirow{2}{*}{Dress} & Lower body fully occluded by a & 0.9279 & 0.9752 & The baseline model shows distorted leg shape; our model reconstructs full body naturally \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \end{tabular}
\end{table} 
Table 2: SSIM-based comparison and visual assessment of baseline and proposed models under occlusions. For the quantitative comparison, we used three metrics: root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). These metrics evaluate how closely the generated images match the corresponding ground-truth images. RMSE quantifies the average pixel-wise difference between the generated and reference images. It is calculated as the square root of the mean of squared differences between pixel values. A lower RMSE indicates higher similarity and better reconstruction accuracy. PSNR measures the ratio between the maximum possible signal (pixel intensity) and the distortion caused by the reconstruction process, expressed in decibels (dB). It is based on the mean square error (MSE) and is commonly used to evaluate the fidelity of lossy image compression or generation. A higher PSNR indicates better image quality. SSIM assesses perceptual image quality by comparing luminance, contrast, and structural information between two images, mimicking human visual perception. SSIM values range from 0 to 1, where values closer to 1 indicate higher structural similarity. The mathematical formulations of these metrics are presented as follows:  
\[RMSE=\sqrt{\frac{1}{N}\frac{N}{i}(\hat{y}_{i}-y_{i})^{2}} \tag{4}\]  
Figure 9: Example results for front data. \[PSNR = 10\cdot\log_{10}(\frac{MAX_{i}^{2}}{MSE}) \tag{5}\] \[SSIM(x,y) = [l(x,y)]\cdot[c(x,y)]\cdot[s(x,y)] \tag{6}\]  
Equation (4) defines RMSE as the square root of the average squared difference between each corresponding pixel of the predicted image \(\hat{y}_{i}\) and the ground-truth image \(y_{i}\), where \(N\) denotes the total number of pixels. A lower RMSE indicates a higher similarity between the two images.