Biomechanical models are used to quantify and optimize human movement in clinical rehabilitation, sports science, and occupational health. Personalizing these models requires accurate identification of anatomical landmarks and body segment parameters, which can be derived from 3D body surface scans. Registering these surfaces to a canonical template is essential for automated landmark detection and model scaling. However, fitting parametric body models to real-world 3D point clouds remains challenging: non-linear optimization can converge to suboptimal solutions when target poses deviate substantially from the default pose of a template. We present BODIESReg, an open-source registration pipeline that addresses this initialization problem by constructing a pose-aligned mesh before distance minimization begins. In automatic mode, BODIESReg can be used for end-to-end registration without user intervention and processes multiple scans in batch. We evaluated BODIESReg on two complementary datasets: CHI3D, a synthetic dataset containing complex human poses, and MorphoMotion, a set of real-world 3D optical scans. Automatic registration succeeded for 82.9% of CHI3D scans and for all MorphoMotion scans. Among successful registrations, mean surface-fit error were below 10 mm across both datasets. For cases where automatic initialization fails, we provide interactive tools for manual pose correction and correspondence selection. BODIESReg supports large-scale registration of 3D body scans for biomechanical research.
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