Human Mesh Modeling for Anny Body
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911328235421696 |
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| author | Brégier, Romain Fiche, Guénolé Bravo-Sánchez, Laura Lucas, Thomas Armando, Matthieu Weinzaepfel, Philippe Rogez, Grégory Baradel, Fabien |
| author_facet | Brégier, Romain Fiche, Guénolé Bravo-Sánchez, Laura Lucas, Thomas Armando, Matthieu Weinzaepfel, Philippe Rogez, Grégory Baradel, Fabien |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03589 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Human Mesh Modeling for Anny Body Brégier, Romain Fiche, Guénolé Bravo-Sánchez, Laura Lucas, Thomas Armando, Matthieu Weinzaepfel, Philippe Rogez, Grégory Baradel, Fabien Computer Vision and Pattern Recognition 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. |
| title | Human Mesh Modeling for Anny Body |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.03589 |