Invariant Teacher and Equivariant Student (ITES)

This repository contains the implementation of the AAAI2021 paper named: Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation. Paper

Abstract: We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network. To further improve the estimation accu- racy, the student network adopts a novel graph convolution network for flexibility to directly estimate the 3D coordinates. Another cycle-consistent architecture promoting 3D rotation-equivariant property is adopted to exploit geometry consistency, together with knowledge distillation from the teacher network to improve the pose estimation performance. We conduct extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D joint prediction error by 11.4% compared to state-of-the-art unsupervised methods and also outperforms many weakly-supervised methods that use side information on Human3.6M.

Requirement

• Python 3.6
• Pytorch 1.1
• Argparse
• Numpy
• Matplotlib

To have a quick installation, you can simply run:

pip install -r requirements.txt

Data preparation

You can find the instructions for setting up the Human3.6M and results of 2D detections in data/README.md. The code for data preparation is borrowed from VideoPose3D. After finishing the data preparation, you should have three .npz files named data_3d_h36m.npz, data_2d_h36m_gt.npz and data_2d_h36m_cpn_ft_h36m_dbb.npz.

Training and Testing

Training from scratch

To train a pose-dictionary-based teacher network using ground-truth 2D detection, run

python train_teacher.py -k gt -e 50 -c checkpoint/teacher

To train a graph-convolution-based student network by a freezed teacher network using ground-truth 2D detection, run

python train_student.py -k gt -e 40 -c checkpoint/student --teacher_checkpoint checkpoint/teacher/ckpt_teacher.bin

if you want to train the student network by your own teacher network model, just modify the --teacher_checkpoint.

Evaluating pretrained models

To evaluate a trained pose-dictionary-based teacher network using ground-truth 2D detection, run

python test_teacher.py -k gt --evaluate -c checkpoint/teacher/ckpt_teacher.bin

or using CPN 2D detection (IMG), run

python test_teacher.py -k cpn_ft_h36m_dbb --evaluate -c checkpoint/teacher/ckpt_teacher.bin

To evaluate a trained graph-convolution-based student network using ground-truth 2D detection, run

python test_student.py -k gt --evaluate -c checkpoint/student/ckpt_student.bin

or using CPN 2D detection (IMG), run

python test_student.py -k cpn_ft_h36m_dbb --evaluate -c checkpoint/student/ckpt_student.bin

Visualization

To get visualization result of a teacher network, simply run

python test_teacher.py -k gt --vis -c checkpoint/teacher/ckpt_teacher.bin

To get visualization result of a stuent network, simply run

python test_student.py -k gt --vis -c checkpoint/teacher/ckpt_student.bin

Acknowledgement

Thanks for the data preprocessing code provided by VideoPose3D, which is source code of the published work 3D human pose estimation in video with temporal convolutions and semi-supervised training.

We also thank for some parts of the network structure provided by the works of C3DPO (paper,code) and SemGCN (paper,code).

Citation

If you use this code, please cite our paper:

@inproceedings{xu2021invariant,
  title={Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation},
  author={Xu, Chenxin and Chen, Siheng and Li, Maosen and Zhang, Ya},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={4},
  pages={3013--3021},
  year={2021}
}