SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation

This work is the extended version of SMPLer-X. This new codebase is designed for easy installation and flexible development, enabling seamless integration of new methods with the pretrained SMPLest-X model.

Useful links

[arXiv]      [Homepage]      [Video]      [SMPLer-X]      [MMHuman3D]      [WHAC]

News

• [2025-02-17] Pretrained model available for download.
• [2025-02-14] 💌💌💌 Brand new codebase released for training, testing and inference.
• [2025-01-20] Paper released on arXiv.
• [2025-01-08] Project page created.

Install

bash scripts/install.sh

Preparation

SMPLest-X pretrained models

• Download the pretrained SMPLest-X-Huge model weight from here (8.2G).
• Place the pretrained weight and respective config file according to the file structure.

Parametric human models

• Download SMPL-X and SMPL body models.

ViT-Pose pretrained models (For training only)

• Follow OSX in preparing pretrained ViTPose models. Download the ViTPose pretrained weights from here.

The file structure should be like:

.
├── assets
├── configs
├── data
│   ├── annot # humandata.npz files
│   ├── cache # cached humandata
│   └── img # original data files
├── datasets
├── demo
├── human_models
│   └── human_model_files # parametric human models
├── main
├── models
├── outputs
│   └── smplest_x_h
├── pretrained_models
│   ├── vitpose_huge.pth # for training only
│   ├── yolov8x.pt # auto download during inference
│   └── smplest_x_h
│       ├── smplest_x_h.pth.tar
│       └── config_base.py
├── scripts
├── utils
├── README.md
└── requirements.txt

Inference

• Place the video for inference under SMPLest-X/demo
• Prepare the pretrained model under SMPLest-X/pretrained_models
• Pretrained YOLO model will be downloaded automatically during the first time usage.
• Inference output will be saved in SMPLest-X/demo

sh scripts/inference.sh {MODEL_DIR} {FILE_NAME} {FPS}

# For inferencing test_video.mp4 (30FPS) with SMPLest-X/pretrained_models/smplest_x_h/smplest_x_h.pth.tar
sh scripts/inference.sh smplest_x_h test_video.mp4 30

Training

bash scripts/train.sh {JOB_NAME} {NUM_GPUS} {CONFIG_FILE}

# For training SMPLest-X-H with 16 GPUS
bash scripts/train.sh smplest_x_h 16 config_smplest_x_h.py

• CONFIG_FILE is the file name under SMPLest-X/config
• Logs and checkpoints will be saved to SMPLest-X/outputs/train_{JOB_NAME}_{DATE_TIME}

Testing

sh scripts/test.sh {TEST_DATSET} {MODEL_DIR} {CKPT_ID}

# For testing the model SMPLest-X/outputs/smplest_x_h/model_dump/snapshot_5.pth.tar 
# on dataset SynHand
sh scripts/test.sh SynHand smplest_x_h 5

• NUM_GPU = 1 is used by default for testing
• Logs and results will be saved to SMPLest-X/outputs/test_{TEST_DATSET}_ep{CKPT_ID}_{DATE_TIME}

FAQ

• How do I animate my virtual characters with SMPLest-X output (like that in the demo video)?
  • We are working on that, please stay tuned! Currently, this repo supports SMPL-X estimation and a simple visualization (overlay of SMPL-X vertices).

Citation

@article{yin2025smplest,
  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},
  journal={arXiv preprint arXiv:2501.09782},
  year={2025}
}