Human Mesh Modeling for Anny Body

Parametric body models provide the structural basis formany human-centric tasks, yet existing models often rely on costly 3Dscans and learned shape spaces that are proprietary and demographi-cally narrow. We introduce Anny, a simple, fully differentiable, and scan-free human body model grounded in anthropometric knowledge from theMakeHuman community. Anny defines a continuous, interpretable shapespace, where phenotype parameters (e.g. gender, age, height, weight)control blendshapes spanning a wide range of human forms—across ages(from infants to elders), body types, and proportions. Calibrated usingWHO population statistics, Anny provides realistic and demographicallygrounded human shape variation within a single unified model. We re-lease the Anny body model and its code under the Apache 2.0 license.Thanks to its openness and semantic control, Anny serves as a versa-tile foundation for 3D human modeling—supporting millimeter-accuratescan fitting, controlled synthetic data generation, and Human Mesh Re-covery (HMR). We further introduce Anny-One, a collection of 780kphotorealistic images generated with Anny, showing that despite its sim-plicity, HMR models trained with Anny can match the performance ofthose trained with scan-based body models.