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@@ -263,10 +263,14 @@ sh slurm_test.sh {JOB_NAME} {NUM_GPU} {TRAIN_OUTPUT_DIR} {CKPT_ID}
```text ```text
# SMPLest-X # SMPLest-X
@article{yin2025smplest, @article{yin2025smplest,
title={SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation}, title={SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation},
author={Yin, Wanqi and Cai, Zhongang and Wang, Ruisi and Zeng, Ailing and Wei, Chen and Sun, Qingping and Mei, Haiyi and Wang, Yanjun and Pang, Hui En and Zhang, Mingyuan and Zhang, Lei and Loy, Chen Change and Yamashita, Atsushi and Yang, Lei and Liu, Ziwei}, author={Yin, Wanqi and Cai, Zhongang and Wang, Ruisi and Zeng, Ailing and Wei, Chen and Sun, Qingping and Mei, Haiyi and Wang, Yanjun and Pang, Hui En and Zhang, Mingyuan and Zhang, Lei and Loy, Chen Change and Yamashita, Atsushi and Yang, Lei and Liu, Ziwei},
journal={arXiv preprint arXiv:2501.09782}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2025} year={2026},
volume={48},
number={2},
pages={1778-1794},
doi={10.1109/TPAMI.2025.3618174}
} }
# SMPLer-X # SMPLer-X
@@ -278,16 +282,29 @@ sh slurm_test.sh {JOB_NAME} {NUM_GPU} {TRAIN_OUTPUT_DIR} {CKPT_ID}
} }
``` ```
## Explore More [SMPLCap](https://github.com/SMPLCap) Projects ## Explore More [Motrix](https://github.com/MotrixLab) Projects
- [TPAMI'25] [SMPLest-X](https://github.com/SMPLCap/SMPLest-X): An extended version of [SMPLer-X](https://github.com/SMPLCap/SMPLer-X) with stronger foundation models. ### Motion Capture
- [ECCV'24] [WHAC](https://github.com/SMPLCap/WHAC): World-grounded human pose and camera estimation from monocular videos. - [SMPL-X] [TPAMI'25] [SMPLest-X](https://github.com/MotrixLab/SMPLest-X): An extended version of [SMPLer-X](https://github.com/MotrixLab/SMPLer-X) with stronger foundation models.
- [CVPR'24] [AiOS](https://github.com/SMPLCap/AiOS): An all-in-one-stage pipeline combining detection and 3D human reconstruction. - [SMPL-X] [NeurIPS'23] [SMPLer-X](https://github.com/MotrixLab/SMPLer-X): Scaling up EHPS towards a family of generalist foundation models.
- [NeurIPS'23] [SMPLer-X](https://github.com/SMPLCap/SMPLer-X): Scaling up EHPS towards a family of generalist foundation models. - [SMPL-X] [ECCV'24] [WHAC](https://github.com/MotrixLab/WHAC): World-grounded human pose and camera estimation from monocular videos.
- [NeurIPS'23] [RoboSMPLX](https://github.com/SMPLCap/RoboSMPLX): A framework to enhance the robustness of - [SMPL-X] [CVPR'24] [AiOS](https://github.com/MotrixLab/AiOS): An all-in-one-stage pipeline combining detection and 3D human reconstruction.
whole-body pose and shape estimation. - [SMPL-X] [NeurIPS'23] [RoboSMPLX](https://github.com/MotrixLab/RoboSMPLX): A framework to enhance the robustness of whole-body pose and shape estimation.
- [ICCV'23] [Zolly](https://github.com/SMPLCap/Zolly): 3D human mesh reconstruction from perspective-distorted images. - [SMPL-X] [ICML'25] [ADHMR](https://github.com/MotrixLab/ADHMR): A framework to align diffusion-based human mesh recovery methods via direct preference optimization.
- [arXiv'23] [PointHPS](https://github.com/SMPLCap/PointHPS): 3D HPS from point clouds captured in real-world settings. - [SMPL-X] [MKA](https://github.com/MotrixLab/MKA): Full-body 3D mesh reconstruction from single- or multi-view RGB videos.
- [NeurIPS'22] [HMR-Benchmarks](https://github.com/SMPLCap/hmr-benchmarks): A comprehensive benchmark of HPS datasets, backbones, and training strategies. - [SMPL] [ICCV'23] [Zolly](https://github.com/MotrixLab/Zolly): 3D human mesh reconstruction from perspective-distorted images.
- [SMPL] [IJCV'26] [PointHPS](https://github.com/MotrixLab/PointHPS): 3D HPS from point clouds captured in real-world settings.
- [SMPL] [NeurIPS'22] [HMR-Benchmarks](https://github.com/MotrixLab/hmr-benchmarks): A comprehensive benchmark of HPS datasets, backbones, and training strategies.
### Motion Generation
- [SMPL-X] [ICLR'26] [ViMoGen](https://github.com/MotrixLab/ViMoGen): A comprehensive framework that transfers knowledge from ViGen to MoGen across data, modeling, and evaluation.
- [SMPL-X] [ECCV'24] [LMM](https://github.com/MotrixLab/LMM): Large Motion Model for Unified Multi-Modal Motion Generation.
- [SMPL-X] [NeurIPS'23] [FineMoGen](https://github.com/MotrixLab/FineMoGen): Fine-Grained Spatio-Temporal Motion Generation and Editing.
- [SMPL] [InfiniteDance](https://github.com/MotrixLab/InfiniteDance): A large-scale 3D dance dataset and an MLLM-based music-to-dance model designed for robust in-the-wild generalization.
- [SMPL] [NeurIPS'23] [InsActor](https://github.com/MotrixLab/insactor): Generating physics-based human motions from language and waypoint conditions via diffusion policies.
- [SMPL] [ICCV'23] [ReMoDiffuse](https://github.com/MotrixLab/ReMoDiffuse): Retrieval-Augmented Motion Diffusion Model.
- [SMPL] [TPAMI'24] [MotionDiffuse](https://github.com/MotrixLab/MotionDiffuse): Text-Driven Human Motion Generation with Diffusion Model.
### Motion Dataset
- [SMPL] [ECCV'22] [HuMMan](https://github.com/MotrixLab/humman_toolbox): Toolbox for HuMMan, a large-scale multi-modal 4D human dataset.
- [SMPLX] [T-PAMI'24] [GTA-Human](https://github.com/MotrixLab/gta-human_toolbox): Toolbox for GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine.