MHR: Momentum Human Rig
Abstract
We present the Meta Momentum Human Rig (MHR), a parametric human body model that combines the decoupled skeleton/shape paradigm of ATLAS with a flexible, modern rig and pose corrective system inspired by the Momentum library. Our model enables expressive, anatomically plausible human animation, supporting both linear and non-linear pose correctives, and is designed for robust integration in AR/VR and graphics pipelines. This model is the output representation of Sam 3D Body a robust promptable foundation model for single-image full-body 3D human mesh recovery (HMR).
Additional Resources
Citation
@misc{MHR:2025,
title={MHR: Momentum Human Rig},
author={Aaron Ferguson and Ahmed A. A. Osman and Berta Bescos and Carsten Stoll and Chris Twigg and Christoph Lassner and David Otte and Eric Vignola and Fabian Prada and Federica Bogo and Igor Santesteban and Javier Romero and Jenna Zarate and Jeongseok Lee and Jinhyung Park and Jinlong Yang and John Doublestein and Kishore Venkateshan and Kris Kitani and Ladislav Kavan and Marco Dal Farra and Matthew Hu and Matthew Cioffi and Michael Fabris and Michael Ranieri and Mohammad Modarres and Petr Kadlecek and Rawal Khirodkar and Rinat Abdrashitov and Romain Prévost and Roman Rajbhandari and Ronald Mallet and Russel Pearsall and Sandy Kao and Sanjeev Kumar and Scott Parrish and Shoou-I Yu and Shunsuke Saito and Takaaki Shiratori and Te-Li Wang and Tony Tung and Yichen Xu and Yuan Dong and Yuhua Chen and Yuanlu Xu and Yuting Ye and Zhongshi Jiang},
year={2025},
eprint={2511.15586},
archivePrefix={arXiv},
primaryClass={cs.GR},
url={https://arxiv.org/abs/2511.15586},
}
@inproceedings{park2025atlas,
title = {ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling},
author = {Park, Jinhyung and Romero, Javier and Saito, Shunsuke and Prada, Fabian and Shiratori, Takaaki and Xu, Yichen and Bogo, Federica and Yu, Shoou-I and Kitani, Kris and Khirodkar, Rawal},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2025}
}