# Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation
## 3 Materials and Methods
The augmented images were produced using fixed noise vectors and BMI values estimated via linear regression to condition the CcGAN. While the generated images generally resemble the real ones and reflect BMI-associated body shape changes, some visual artifacts or soft boundaries—particularly around the abdomen or arms—may be observed. These examples are intended to provide readers with an intuitive understanding of the characteristics of both the original and augmented datasets used for training. Each layer uses a 4 \(\times\) 4 kernel, stride of 2, and padding of 1. Batch normalization and ReLU activation are applied to all layers except the last, which uses a tanh activation function to produce a 3-channel RGB image. The discriminator evaluates the realism of the image using a CNN that processes the image and embedded label through parallel streams before concatenation and final classification. The discriminator consists of five convolutional layers with filter sizes of 64, 128, 256, 512, and 1. All layers use a 4 \(\times\) 4 kernel. The first three layers apply a stride of 2, and the last two apply a stride of 1. Instance normalization is applied from the second layer onward, and LeakyReLU (slope = 0.2) is used as the activ  
Figure 4: Architecture of the Continuous Conditional Generative Adversarial Network (CcGAN) used for data augmentation. The generator receives a noise vector z and a continuous BMI value (before or after liposome), which is embedded into a fixed-dimensional vector and concatenated with the noise input. The generator outputs synthetic images corresponding to the specified BMI. A linear regression model is used to derive the “after” BMI from a sampled “before” BMI, allowing for paired generation of pre- and post-liposuction images using the same noise. The discriminator receives real or generated images and evaluates their realism while simultaneously verifying whether the image condition (BMI) matches the embedded label. This architecture enables realistic and condition-consistent data augmentation for both pre- and post-liposuction images. Figure 3: (**a**) Histogram showing the distribution of BMI before and after liposome. The post-liposuction BMI values are generally lower, indicating an overall reduction in body mass index. (**b**) Scatter plot of pre- and post-liposuction BMI, revealing a strong linear correlation (Pearson r = 0.9552). This result supports the use of linear regression to predict post-liposuction BMI from pre-liposuction values for data augmentation. layer uses a sigmoid function to classify real vs. fake. More detailed descriptions of the generator and discriminator structures are provided in the later section titled 'Network Architecture: Pix2 Pix'. The generator generates images based on random noise z and a continuous label. The continuous label is transformed to a specific dimension through embedding before being used. Unlike Pix2 Pix, CeGAN does not require an input image, as it synthesizes images from a random noise vector and condition value. Therefore, it does not employ an encoder-decoder structure such as U-Net. Instead, it uses a stack of transposed convolutional layers to upsample the input latent representation into a full-resolution image gradually. We performed data augmentation to create pre-liposuction and post-liposuction images for the same person. While the noise z was fixed, the BMI values before and after liposuction were utilized differently to generate images of a person before and after liposuction, respectively. Fixing the noise vector z ensures that the identity-related latent features remain constant across the generated pre- and post-liposuction images. This design allows the BMI condition to modulate only the morphological aspects relevant to fat reduction, thereby enabling identity-consistent yet BMI-dependent image generation. Such a strategy aligns with prior work--InfoGAN [31]--that explicitly separates incompressible noise from semantic latent codes to disentangle identity and attribute factors in conditional generative models.