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Input point clouds are from HuMMan-Point (Coming Soon).
@article{cai2023pointhps,
title = {PointHPS: Cascaded 3D Human Pose and Shape Estimation from Point Clouds},
author = {Cai, Zhongang and Pan, Liang, and Wei, Chen and Yin, Wanqi, and Hong, Fangzhou and Zhang, Mingyuan and Loy, Chen Change, and Yang, Lei, and Liu, Ziwei},
year = {2023},
journal = {arXiv preprint arXiv:2308.14492}
}
Explore More SMPLCap Projects
- [arXiv'25] SMPLest-X: An extended version of SMPLer-X with stronger foundation models.
- [ECCV'24] WHAC: World-grounded human pose and camera estimation from monocular videos.
- [CVPR'24] AiOS: An all-in-one-stage pipeline combining detection and 3D human reconstruction.
- [NeurIPS'23] SMPLer-X: Scaling up EHPS towards a family of generalist foundation models.
- [NeurIPS'23] RoboSMPLX: A framework to enhance the robustness of whole-body pose and shape estimation.
- [ICCV'23] Zolly: 3D human mesh reconstruction from perspective-distorted images.
- [arXiv'23] PointHPS: 3D HPS from point clouds captured in real-world settings.
- [NeurIPS'22] HMR-Benchmarks: A comprehensive benchmark of HPS datasets, backbones, and training strategies.





