# Predicting Post-Liposuction Body Shape Using RGB Image-to-Image Translation
## 3 Materials and Methods
Pix2 Pix comprises a generator and a discriminator, which are trained in an adversarial manner, as shown in Figure 5.  
The generator of the Pix2 Pix model is implemented as a Convolutional Neural Network (CNN) based on a U-Net architecture consisting of eight encoder layers and eight decoder layers. The encoder progressively reduces the spatial resolution of the input image (256 \(\times\) 256 pixels) to 1 \(\times\) 1, using convolutional layers with filter sizes of 64, 128, 256, 512, 512, 512, and 512. Each encoder layer applies a convolution operation with a kernel size of 4 \(\times\) 4, stride of 2, and padding of 1. Batch normalization and LeakyReLU activation (with a slope of 0.2) are applied to all encoder layers except the first. The decoder mirrors the encoder's structure and restores resolution using transposed convolutions with a kernel size of 4 \(\times\) 4, stride of 2, and padding of 1. Each decoder layer uses ReLU activation and  
Figure 5: Overview of the Pix2 Pix Architecture used for predicting post-liposuction images. During training, the generator learns to produce realistic post-liposuction images from pre-liposuction inputs, which are then compared with ground truth images. The discriminator receives both real (input + ground truth) and fake (input + generated) image pairs to distinguish between authentic and generated outputs. During inference, only the generator is used to produce predictions. This architecture is optimized using a combination of adversarial loss and reconstruction loss functions. batch normalization, except for the last layer, which applies a tanh activation function without normalization to produce a 3-channel RGB output. Specifically, the decoder consists of eight layers with filter sizes of 512, 512, 512, 512, 512, 256, 128, 64, and 3, corresponding to each upsampling stage. In accordance with the original Pix2 Pix implementation, dropout with a rate of 0.5 is applied to the first three decoder layers during training to improve generalization and prevent overfitting. Skip connections are implemented via channel-wise concatenation between each encoder layer and its corresponding decoder layer, allowing spatial information and fine-grained details to be preserved throughout the reconstruction process. This encoder-decoder CNN structure allows feature maps and spatial information to be effectively transmitted without loss [38; 39; 40]. The discriminator of the Pix2 Pix model is implemented as a Convolutional Neural Network (CNN) and follows the PatchGAN architecture, which evaluates the realism of overlapping local image patches instead of assessing the entire image at once. This design is particularly effective for capturing high-frequency visual details, such as contours and textures, that are essential for generating realistic images. The discriminator receives as input a 256 \(\times\) 256 \(\times\) 6 tensor formed by concatenating a pre-liposuction image and either a real or generated post-liposuction image along the channel dimension. The network consists of five convolutional layers with filter sizes of 64, 128, 256, 512, and 1, respectively. All layers use a 4 \(\times\) 4 kernel, with the first three layers applying a stride of 2 and the last two a stride of 1. A padding of 1 is applied to all layers to maintain spatial consistency throughout the network. Each layer uses a LeakyReLU activation function (with a slope of 0.2), and instance normalization is applied from the second layer onward to stabilize training and improve generalization. The final output layer uses a sigmoid activation function to predict the authenticity of each local patch. Unlike traditional discriminators that produce a single real/fake score for the whole image, PatchGAN outputs a probability map--for example, 30 \(\times\) 30--where each value represents the likelihood that a particular image patch is real.