A Rule-Based Framework for Clothing Fit Recommendation from 3D Body Reconstruction

Abstract

This research presents a comprehensive framework for body size estimation that accurately derives anthropometric measurements—specifically, the circumferences of the waist and hips—from a singular image by utilizing OpenPose for joint localization and SMPLify-X for precise 3D body modeling. The proposed methodology involves projecting the generated three-dimensional model onto a horizontal plane and applying a convex hull geometric assessment to extract relevant body measurements. These derived measurements are then classified into standardized clothing size predictions (XS–XL) via a transparent rule-based classification system suitable for e-commerce sizing and virtual fitting applications. Empirical validation conducted on the Agora dataset substantiated the framework’s reliability across diverse body types, demonstrating strong consistency with industry sizing standards. The method is non-intrusive and interpretable, effectively addressing practical challenges in automated human pose estimation for retail contexts. Limitations include constraints related to body posture and potential clothing interference; however, the modular design enables enhancements such as integrating chest circumference measurements and mobile deployment. This scholarly contribution thus provides a robust, accessible solution for automated, image-based clothing size recommendations.