MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation [CVPR 2022]

MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation,
Wenhao Li, Hong Liu, Hao Tang, Pichao Wang, Luc Van Gool,
In Conference on Computer Vision and Pattern Recognition (CVPR), 2022

Updates

• 03/24/2022: Demo and in-the-wild inference code is released!

Installation

• Create a conda environment: conda create -n mhformer python=3.6
• Download cudatoolkit=11.0 from here and install
• pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
• pip install -r requirements.txt

Dataset setup

Please download the dataset from Human3.6M website and refer to VideoPose3D to set up the Human3.6M dataset ('./dataset' directory). Or you can download the processed data from here.

${POSE_ROOT}/
|-- dataset
|   |-- data_3d_h36m.npz
|   |-- data_2d_h36m_gt.npz
|   |-- data_2d_h36m_cpn_ft_h36m_dbb.npz

Download pretrained model

The pretrained model can be found in here, please download it and put it in the './checkpoint' directory.

Test the model

To test on pretrained model on Human3.6M:

python main.py --test --reload --previous_dir 'checkpoint/pretrained'

Here, we compare our MHFormer with recent state-of-the-art methods on Human3.6M dataset. Evaluation metric is Mean Per Joint Position Error (MPJPE) in mm​.

Models	MPJPE
VideoPose3D	46.8
PoseFormer	44.3
MHFormer	43.0

Train the model

To train on Human3.6M:

python main.py

Demo

First, you need to download YOLOv3 and HRNet pretrained models here and put it in the './demo/lib/checkpoint' directory. Then, you need to put your in-the-wild videos in the './demo/video' directory.

Run the command below:

python demo/vis.py --video sample_video.mp4

Sample demo output:

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{li2022mhformer,
  title={MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation},
  author={Li, Wenhao and Liu, Hong and Tang, Hao and Wang, Pichao and Van Gool, Luc},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

Acknowledgement

Our code is extended from the following repositories. We thank the authors for releasing the codes.

• ST-GCN
• VideoPose3D
• 3d-pose-baseline
• 3d_pose_baseline_pytorch
• StridedTransformer-Pose3D

Licence

This project is licensed under the terms of the MIT license.