# Pose-Driven Body Shape Prediction Algorithm Based on the Conditional GAN
## 4 Results
Equation (5) defines PSNR as a logarithmic measure of the ratio between the maximum possible pixel value \(MAX_{i}\) and the mean square error (MSE) between the predicted and ground-truth images. A higher PSNR value indicates less distortion and better image quality. Equation (6) expresses SSIM as the product of three components: luminance comparison \(l(x,y)\), contrast comparison \(c(x,y)\), and structural comparison \(s(x,y)\). Each term measures the similarity of brightness, contrast, and structural information, respectively, between two images. SSIM ranges from 0 to 1, where a value closer to 1 indicates higher perceptual similarity. The constants used within each component help stabilize the division operations, especially when denominators are near zero. Figure 10: Example results for side data. Table 3 compares the front full-body images generated by the proposed Pix2 Body model and the baseline Pix2 Pix model using three evaluation metrics: RMSE, PSNR, and SSIM. As shown in Table 3, the RMSE difference is 0.0083, the PSNR difference is 0.9659, and the SSIM difference is 0.0406, with Pix2 Body outperforming the baseline across all metrics. Although the numerical differences in RMSE and SSIM appear modest, this can be attributed to the minimal luminance and contrast variation between the two models' outputs, as illustrated in the qualitative comparison in Figures 9 and 10. Furthermore, since the test set contains a high proportion of static poses, the baseline model was able to achieve relatively favorable results. To validate the statistical significance of the observed improvements, we collected sample-wise RMSE, PSNR, and SSIM values across the entire test set and conducted paired \(t\)-tests comparing the performance of Pix2 Pix and Pix2 Body. The results revealed statistically significant differences in all three metrics: RMSE: \(p=0.0032\), PSNR: \(p=0.0011\), SSIM: \(p=0.0026\). These findings demonstrate that the improvements achieved by Pix2 Body are not only visually and qualitatively evident but also quantitatively and statistically significant, reinforcing the effectiveness of the proposed method for reconstructing human body shapes from clothed RGB images. Subsequently, we performed a generalization test on a typical human body rather than on synthetic data, which had already been employed for training. Figure 11 depicts the experimental outcomes of body shape generation tests on one man and one woman. This study was approved by the Institutional Review Boards of Dongguk University (DUIRB-202202-14), and the procedures performed in the study complied with the principles of the Declaration of Helsinki. All study participants provided written informed consent for their participation and publication of their images, and the study design was approved by the ethics review board: [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. Body joint coordinates were extracted from the normalized full-body image of a clothed subject and used as input features, following the same procedure as in the previous qualitative evaluation. The results of Pix2 Body, the model proposed in this study, had fewer missing areas than those of the baseline model and a more detailed representation of body bends, as demonstrated in Figure 11. Pix2 Body correctly inferred the positions of the knees and the orientations of the arms and legs based on the positions, sizes, and orientations of the joint points of the body and organically depicted body shapes such as the torso and thighs. However, the accuracy was lower than that on the training dataset, which was a synthetic dataset. This study provides four probable explanations for the reduced accuracy demonstrated in the generalization test compared to that on the synthetic dataset. First, the synthetic dataset has consistent subject locations and angles, whereas the real-world full-body images had inconsistent shooting angles and measuring distances, resulting in erroneous results. Second, there was a discrepancy in image quality.