HTNet: Human Topology Aware Network for 3D Human Pose Estimation,
Jialun Cai, Hong Liu, Runwei Ding , Wenhao Li, Jianbing Wu, Miaoju Ban
In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023
Protocol 1 (mean per-joint position error) when 2D keypoints detected by CPN and the ground truth of 2D poses.
| Method | Train Epochs | MPJPE (CPN) | MPJPE (GT) |
|---|---|---|---|
| GraFormer | 50 | 51.8 mm | 35.2 mm |
| MGCN (w/refine) | 50 | 49.4 mm | 33.5 mm |
| HTNet | 15 | 48.9 mm | 34.0 mm |
| HTNet (w/refine) | 15 | 47.6 mm | 31.9 mm |
Special thanks to MHFormer, we have completed a beginner's guide for image-based pose estimation. Only three steps that poses can be generated for your own images:(1) Download pretrained models (Yolov3 and HRNet) here and put them in the './demo/lib/checkpoint'; (2)download pretrained model and put it in the './ckpt' directory; (3) Put your own images in the './demo/figure', and run:
python demo/vis.py
Then you can obtain the visualized pose in the "./demo/output", like:
To get started as quickly as possible, follow the instructions in this section. This should allow you train a model from scratch, test our pretrained models.
Make sure you have the following dependencies installed before proceeding:
- Python 3.7+
- PyTorch >= 1.10.0 To setup the environment:
pip install -r requirement.txtPlease download the dataset here and refer to VideoPose3D to set up the Human3.6M dataset ('./dataset' directory).
${POSE_ROOT}/
|-- dataset
| |-- data_3d_h36m.npz
| |-- data_2d_h36m_gt.npz
| |-- data_2d_h36m_cpn_ft_h36m_dbb.npzThe pretrained model is here, please download it and put it in the './ckpt' directory. To achieve the performance in the paper, run:
python main.py --reload --previous_dir "ckpt/cpn"
If you want to train your own model, run:
python main.py --train -n "your_model_name"
Our code is extended from the following repositories. We thank the authors for releasing the codes.
This project is licensed under the terms of the MIT license.