# Pose-Driven Body Shape Prediction Algorithm Based on the Conditional GAN
## 1 Introduction
However, this approach requires high-resolution 3D ground-truth data obtained through detailed full-body scans, which are typically available only in synthetic datasets or in strictly controlled laboratory environments. Unlike prior works that relied on texture-based cues or required skin-tight garments, our model explicitly integrates joint-based spatial structure and surface geometry to improve reconstruction accuracy under occlusions. Therefore, this study sought to reduce training time by developing an algorithm that could estimate the human body while the body is covered by various garments. To determine the characteristics of a body wearing clothes, we used the Human Body Estimator. Body features can be identified based on derivations of the coordinates of the major joints of the body [33], which can be used to identify body information, such as regarding the arms, legs, shoulders, and pelvis, that is unique to each individual. In this study, we aimed to generate body shapes with no missing areas by including position information for unstructured body elements, such as body angles and positions, when deriving body features. Building on this motivation, we propose Pix2 Body, a pose-guided conditional generative adversarial network (GAN) that reconstructs full 3D human body shapes from a single RGB image of a clothed person. A conditional generative adversarial network (cGAN) is a variant of a GAN in which both the generator and the discriminator are conditioned on additional inputs such as pose, semantic maps, or other structural priors. This allows the network to generate context-aware outputs and is particularly useful for structured prediction tasks. By leveraging 2D joint coordinates and both front-view and back-view normal maps as auxiliary inputs, our method achieves accurate and realistic reconstruction even in occluded regions, such as folded arms or skirt-covered legs. Unlike conventional approaches such as Pix2 Pix that rely solely on texture mappings, Pix2 Body incorporates spatial priors derived from pose and surface cues, enabling improved structural reasoning. Furthermore, unlike recent state-of-the-art models such as PIFuHD, ICON, or ROMP that often require multi-view images, dense supervision, or parametric mesh fitting, Pix2 Body is designed to operate with only single-view RGB images and lightweight annotations. This makes our method highly practical and scalable for real-world applications, especially in scenarios with limited data or real-time processing constraints. This hybrid design enhances both reconstruction accuracy and surface detail quality, even under clothing-induced occlusion. We validated the effectiveness of our framework through experiments on a custom dataset of 190 clothed individuals with diverse body types and poses. The results show that Pix2 Body delivers superior performance in both quantitative metrics (e.g., SSIM) and qualitative realism, providing a practical and efficient solution for non-invasive, indirect body measurement. While previous pose-guided or SMPL-based frameworks often required 3D mesh supervision or optimization-based inference, Pix2 Body introduces a lightweight approach that combines pose estimation and normal maps within a conditional GAN framework. This combination has been relatively underexplored in prior research, and our work aimed to implement it in a practical and application-oriented manner. The main contributions of this study are summarized as follows:  
* We propose Pix2 Body, a pose-guided conditional GAN that reconstructs full-body 3D shapes from clothed RGB images. * Our model integrates 2D joint coordinates and front/back normal maps to enhance performance in occluded areas. * The method supports non-invasive body modeling without requiring skin-tight clothing. * We constructed a dataset of 190 individuals with diverse poses and garments for robust evaluation. * The experimental results confirm the model's capability to infer realistic body shapes even in challenging conditions. The remainder of this paper is structured as follows.