Human Mesh Modeling for Anny Body

TL;DR

Anny is a demographically grounded, fully differentiable human body model that enables realistic, controllable 3D human shape modeling without costly scans, supporting various applications like fitting, synthesis, and recovery.

Contribution

We introduce Anny, a simple, open, and anthropometrically calibrated human body model with a continuous shape space controlled by semantic parameters.

Findings

Anny can generate diverse, realistic human shapes across ages and body types.

Models trained with Anny achieve performance comparable to scan-based models.

Anny's open-source code facilitates accessible 3D human modeling.

Abstract

Parametric body models provide the structural basis for many human-centric tasks, yet existing models often rely on costly 3D scans and learned shape spaces that are proprietary and demographically narrow. We introduce Anny, a simple, fully differentiable, and scan-free human body model grounded in anthropometric knowledge from the MakeHuman community. Anny defines a continuous, interpretable shape space, 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 using WHO population statistics, it provides realistic and demographically grounded human shape variation within a single unified model. Thanks to its openness and semantic control, Anny serves as a versatile foundation for 3D human modeling--supporting millimeter-accurate scan fitting, controlled synthetic data generation, and Human Mesh Recovery (HMR). We further introduce Anny-One, a collection of 800k photorealistic images generated with Anny, showing that despite its simplicity, HMR models trained with Anny can match the performance of those trained with scan-based body models. The Anny body model and its code are released under the Apache 2.0 license, making Anny an accessible foundation for human-centric 3D modeling.