AI in Medicine
Authors: Maja Nikolic (RISC Software GmbH) , Sophie Kaltenleithner (RISC Software GmbH) , Ulrich Bodenhofer (University of Applied Sciences Upper Austria) , Michael Giretzlehner (RISC Software GmbH)
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.
Keywords:
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).