# Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation
## 4 Results
Input images were resized to 256 \(\times\) 256 pixels and normalized using ImageNet statistics, specifically by subtracting the channel-wise mean [0.485, 0.456, 0.406] and dividing by the standard deviation [0.229, 0.224, 0.225]. These preprocessing settings ensured consistency with the pretrained VGG16 model used in the perceptual loss calculation. These settings enabled stable training and ensured that the model captured both pixel-level and structural differences between pre- and post-liposuction images. Figure 10 provides several examples of predicted post-liposuction images generated by the Pix2 Pix model trained with L1 and perceptual loss. This blurring was primarily caused by inconsistent arm placement during image acquisition. Although the camera height and distance were fixed, no explicit instructions were given regarding arm angle or position, leading to variations between subjects. As a result, the boundary between the arms and torso became ambiguous, especially in the abdominal region--reducing the clarity of the predicted contour. This represents a failure  
Figure 9: Qualitative comparison of predicted post-liposuction images with and without data augmentation. This figure compares the predicted results from models trained without augmentation (second column) and with CcGAN-based data augmentation (third column), given the same pre-liposuction input images (first column). Ground truth post-liposuction images are shown in the last column for reference. Without augmentation, the generated images exhibit significant artifacts and structural inconsistencies, while the augmented model produces more realistic contours despite minor blurring because of arm pose variability. These results demonstrate that data augmentation helps improve prediction fidelity and visual quality. case where insufficient posture standardization during data collection negatively impacted prediction accuracy. The corresponding results are shown in Figure 11.  
Another failure case was found in patients with relatively low BMI. In such cases, the physical change after liposuction is often minimal and difficult to detect visually. As a result, the model occasionally failed to generate images with noticeable differences, producing predictions nearly identical to the preoperative image. This limitation is illustrated in Figure 12.  
We compared the results of the models based on L1 loss and perceptual loss. The model with the smallest L1 loss was evaluated on the test dataset. As shown in Figure 9, when L1 loss was used, the images tended to be blurry. This occurred because edge details and other information were not considered, as the model focused solely on minimizing pixel-wise errors. The images generated by the model based on perceptual loss were less blurry. However, in the case of the first subject, when only perceptual loss is used, the body shape exhibited minimal change. By comparison, when L1 loss and perceptual loss were used together, the generated images were even less blurry, and body shape changes were accurately reflected. As shown in Figure 13, the comparison of predicted postoperative images generated by different loss functions demonstrates the differences clearly. The  
Figure 10: Examples of post-liposuction prediction results generated by the Pix2 Pix model trained with L1 and perceptual loss. This figure presents qualitative examples comparing preoperative images (**left column**) and corresponding predicted postoperative images (**right column**), generated using the Pix2 Pix model trained with a combination of L1 and perceptual loss. The original images were captured before liposuction, and the predicted images aim to simulate postoperative body shapes based solely on the input pre-liposuction image. The output generated reflects changes primarily in the abdominal and waist regions. Minor blurring can be observed, particularly around the arms, because of variability in arm positioning during data acquisition.