AI in Medicine

Generating Realistic and Accurate SMPL Body Shapes from Anthropometric Measurements

---

Abstract

Accurate human 3D avatars are essential for various medical applications, such as pain visualization and burn size estimation. Generating these avatars from simple anthropometric measurements offers a cheaper and more practical alternative to conventional 3D body scanners and image-based reconstruction methods, especially when patients have limited mobility. In this work, we investigate the reliability of predicting Skinned Multi-Person Linear model (SMPL) shape parameters from anthropometric measurements in the presence of real-world noise. We further introduce \beta-likelihood to quantify the anatomical plausibility of generated shapes against a learned distribution. Multiple regression models are evaluated on two external datasets, revealing a clear trade-off between metric accuracy and shape plausibility. The results indicate that regularized regression models are best suited to balance this trade-off when dealing with real-world measurement noise.

How to Cite:

Nikolic, M., Kaltenleithner, S., Bodenhofer, U. & Giretzlehner, M., (2026) “Generating Realistic and Accurate SMPL Body Shapes from Anthropometric Measurements”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 6-10.