# Pose-Driven Body Shape Prediction Algorithm Based on the Conditional GAN
## 5 Discussion  
In this study, we developed an algorithm that predicts naked body shapes by calculating major joint positions from two-dimensional complete clothed-body photos. The newly developed method generates body features while accounting for the diversity of human body shapes that exist among different people. Thus, the algorithm proposed in this research can be used to infer the unclothed body shape of a clothed individual without the need to implement constraints such as the removal of clothes or the wearing of clothes that stick to the body. Conventional generation algorithms do not include body shape information, making it impossible to generate unstructured parts with inconsistent body angles and positions, or body parts that are difficult to predict because they are hidden by clothes, etc. To address these limitations, this study extracted body joint points (body key points) via pose estimation and incorporated body shape information. Body key points can be used to deduce not only body features but also body orientation and location. Unlike conventional generation methods, the developed algorithm can infer and construct disrobed body shapes with no missing parts. Although our method demonstrates improved performance over Pix2 Pix by leveraging pose information, the scope of comparative evaluation remains limited. Several notable parametric reconstruction frameworks--such as SPIN, GraphCMR, and SMPLify-X--were not included in our evaluation due to practical constraints. In addition, recent non-parametric methods such as PIFuHD, ICON, and ROMP have shown strong performance in human shape reconstruction. However, these models typically rely on multi-view inputs, dense 3D supervision, or pretrained SMPL parameters, making them less suitable for lightweight or real-time applications. In contrast, Pix2 Body prioritizes ease of deployment by requiring only a single-view RGB image, 2D keypoints, and front/back normal maps. This enables practical application in uncontrolled environments without the need for expensive 3D ground-truth scans or complex rendering pipelines. While Pix2 Pix can be easily implemented in modern GPU environments using recent versions of PyTorch (e.g., v1.13.1), these parametric models require legacy PyTorch versions (e.g., v0.4.1)and dependencies (e.g., Chumpy, OpenDR, SMPL-X model license) that are difficult to integrate into the current development pipelines. Moreover, SMPLify-X involves \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Model** & **Key Datasets** & **Key Quantitative Metrics** \\ \hline \multirow{4}{*}{SMPL-based} & SMPLify & - LSP & - PA-MPJPE: \(\sim\)82.3 mm \\  & SMPLify-X & - Human3.6M & - PVE: 52.9 mm \\  & \multirow{2}{*}{Pose2 Mesh} & - EHF & - MPJPE: 62.6 mm \\  & & - Human3.6M & - MPJPE: 64.9 mm \\  & & - MPI-INF-3 DHP & - PA-MPJPE: 47.0 mm \\  & \multirow{2}{*}{Tex2 Shape} & - ShapeNet (synthetic) & - Chamfer distance: \(\sim\)3.7 \\  & & - FID: 49.8 & - FID: 49.8 \\  & \multirow{2}{*}{PIFuHD} & - RenderPeople & - Chamfer-L1: 1.20 cm \\  & & - DeepHuman & - P2S: 0.81 cm \\  & \multirow{2}{*}{ICON} & - THuman2.0 & - Chamfer-L1: 0.84 cm \\  & & - RenderPeople & - Normal consistency: 0.94 \\  & & - SSIM: 0.08 \\ Our model & Pix2 Body & - SHADER (Synthetic) & - PSNR: 21.76 \\  & & & - RMSE: 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of Pix2 Body and representative 3D human body reconstruction methods. optimization-based inference, which is computationally expensive and unsuitable for our real time-oriented framework. In future work, we aim to address these limitations by designing a unified evaluation pipeline that incorporates mesh-based and parametric representations, possibly using intermediate metrics such as PVE (per-vertex error) or chamfer distance. Additionally, we plan to include recent SOTA models such as ICON, PIFuHD, and ROMP for more comprehensive benchmarking.