PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation

Code repository for the paper:
PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation
Kehong Gong*, Jianfeng Zhang*, Jiashi Feng
CVPR 2021 (oral presentation)
[paper] [project page (coming soon)]

Installation

The experiments are conducted on Ubuntu 16.04, with Python version 3.6.9, and PyTorch version 1.0.1.post2.

To setup the environment:

cd PoseAug
conda create -n poseaug python=3.6.9
conda activate poseaug
pip install -r requirements.txt

Prepare dataset

• Please refer to DATASETS.md for the preparation of the dataset files.

Run training code

• There are 32 experiments in total (16 for baseline training, 16 for PoseAug training), including four pose estimators (SemGCN, SimpleBaseline, ST-GCN, VideoPose) and four 2D pose settings (Ground Truth, CPN, DET, HR-Net).
• The training procedure contains two steps: pretrain the baseline models and then train these baseline models with PoseAug.
• We also provide pretrained baseline and poseaug models (link) for further training and evaluation.

To pretrain the baseline model,

# gcn
python3 run_baseline.py --note pretrain --dropout 0 --lr 2e-2 --epochs 100 --posenet_name 'gcn' --checkpoint './checkpoint/pretrain_baseline' --keypoints gt
python3 run_baseline.py --note pretrain --dropout 0 --lr 2e-2 --epochs 100 --posenet_name 'gcn' --checkpoint './checkpoint/pretrain_baseline' --keypoints cpn_ft_h36m_dbb
python3 run_baseline.py --note pretrain --dropout 0 --lr 2e-2 --epochs 100 --posenet_name 'gcn' --checkpoint './checkpoint/pretrain_baseline' --keypoints detectron_ft_h36m
python3 run_baseline.py --note pretrain --dropout 0 --lr 2e-2 --epochs 100 --posenet_name 'gcn' --checkpoint './checkpoint/pretrain_baseline' --keypoints hr

# videopose
python3 run_baseline.py --note pretrain --lr 1e-3 --posenet_name 'videopose' --checkpoint './checkpoint/pretrain_baseline' --keypoints gt
python3 run_baseline.py --note pretrain --lr 1e-3 --posenet_name 'videopose' --checkpoint './checkpoint/pretrain_baseline' --keypoints cpn_ft_h36m_dbb
python3 run_baseline.py --note pretrain --lr 1e-3 --posenet_name 'videopose' --checkpoint './checkpoint/pretrain_baseline' --keypoints detectron_ft_h36m
python3 run_baseline.py --note pretrain --lr 1e-3 --posenet_name 'videopose' --checkpoint './checkpoint/pretrain_baseline' --keypoints hr

# mlp
python3 run_baseline.py --note pretrain --lr 1e-3 --stages 2 --posenet_name 'mlp' --checkpoint './checkpoint/pretrain_baseline' --keypoints gt
python3 run_baseline.py --note pretrain --lr 1e-3 --stages 2 --posenet_name 'mlp' --checkpoint './checkpoint/pretrain_baseline' --keypoints cpn_ft_h36m_dbb
python3 run_baseline.py --note pretrain --lr 1e-3 --stages 2 --posenet_name 'mlp' --checkpoint './checkpoint/pretrain_baseline' --keypoints detectron_ft_h36m
python3 run_baseline.py --note pretrain --lr 1e-3 --stages 2 --posenet_name 'mlp' --checkpoint './checkpoint/pretrain_baseline' --keypoints hr

# st-gcn
python3 run_baseline.py --note pretrain --dropout -1 --lr 1e-3 --posenet_name 'stgcn' --checkpoint './checkpoint/pretrain_baseline' --keypoints gt
python3 run_baseline.py --note pretrain --dropout -1 --lr 1e-3 --posenet_name 'stgcn' --checkpoint './checkpoint/pretrain_baseline' --keypoints cpn_ft_h36m_dbb
python3 run_baseline.py --note pretrain --dropout -1 --lr 1e-3 --posenet_name 'stgcn' --checkpoint './checkpoint/pretrain_baseline' --keypoints detectron_ft_h36m
python3 run_baseline.py --note pretrain --dropout -1 --lr 1e-3 --posenet_name 'stgcn' --checkpoint './checkpoint/pretrain_baseline' --keypoints hr
# Note: for st-gcn, dropout is set to -1, representing the default dropout setting used in the original code (different layers using different dropout values).

To train the baseline model with PoseAug:

# gcn
python3 run_poseaug.py --note poseaug --dropout 0 --posenet_name 'gcn' --lr_p 1e-3 --checkpoint './checkpoint/poseaug' --keypoints gt
python3 run_poseaug.py --note poseaug --dropout 0 --posenet_name 'gcn' --lr_p 1e-3 --checkpoint './checkpoint/poseaug' --keypoints cpn_ft_h36m_dbb
python3 run_poseaug.py --note poseaug --dropout 0 --posenet_name 'gcn' --lr_p 1e-3 --checkpoint './checkpoint/poseaug' --keypoints detectron_ft_h36m
python3 run_poseaug.py --note poseaug --dropout 0 --posenet_name 'gcn' --lr_p 1e-3 --checkpoint './checkpoint/poseaug' --keypoints hr

# video
python3 run_poseaug.py --note poseaug --posenet_name 'videopose' --lr_p 1e-4 --checkpoint './checkpoint/poseaug' --keypoints gt
python3 run_poseaug.py --note poseaug --posenet_name 'videopose' --lr_p 1e-4 --checkpoint './checkpoint/poseaug' --keypoints cpn_ft_h36m_dbb
python3 run_poseaug.py --note poseaug --posenet_name 'videopose' --lr_p 1e-4 --checkpoint './checkpoint/poseaug' --keypoints detectron_ft_h36m
python3 run_poseaug.py --note poseaug --posenet_name 'videopose' --lr_p 1e-4 --checkpoint './checkpoint/poseaug' --keypoints hr

# mlp
python3 run_poseaug.py --note poseaug --posenet_name 'mlp' --lr_p 1e-4 --stages 2 --checkpoint './checkpoint/poseaug' --keypoints gt
python3 run_poseaug.py --note poseaug --posenet_name 'mlp' --lr_p 1e-4 --stages 2 --checkpoint './checkpoint/poseaug' --keypoints cpn_ft_h36m_dbb
python3 run_poseaug.py --note poseaug --posenet_name 'mlp' --lr_p 1e-4 --stages 2 --checkpoint './checkpoint/poseaug' --keypoints detectron_ft_h36m
python3 run_poseaug.py --note poseaug --posenet_name 'mlp' --lr_p 1e-4 --stages 2 --checkpoint './checkpoint/poseaug' --keypoints hr

# st-gcn
python3 run_poseaug.py --note poseaug --dropout 0 --posenet_name 'stgcn' --lr_p 1e-4 --checkpoint './checkpoint/poseaug' --keypoints gt
python3 run_poseaug.py --note poseaug --dropout 0 --posenet_name 'stgcn' --lr_p 1e-4 --checkpoint './checkpoint/poseaug' --keypoints cpn_ft_h36m_dbb
python3 run_poseaug.py --note poseaug --dropout 0 --posenet_name 'stgcn' --lr_p 1e-4 --checkpoint './checkpoint/poseaug' --keypoints detectron_ft_h36m
python3 run_poseaug.py --note poseaug --dropout 0 --posenet_name 'stgcn' --lr_p 1e-4 --checkpoint './checkpoint/poseaug' --keypoints hr

All the checkpoints, evaluation results and logs will be saved to ./checkpoint. You can use tensorboard to monitor the training process:

cd ./checkpoint/poseaug
tensorboard --logdir=/path/to/eventfile

Comment:

• For simplicity, hyper-param for different 2D pose settings are the same. If you want to explore better performance for specific setting, please try changing the hyper-param.
• The GAN training may collapse, change the hyper-param (e.g., random_seed) and re-train the models will solve the problem.

Run evaluation code

python3 run_evaluate.py --posenet_name 'videopose' --keypoints gt --evaluate '/path/to/checkpoint'

We provide a checkpoint/PoseAug_result_summary.ipynb, which can generate the result summary table for all 16 experiments.

Run inference code

We provide an inference code here. Please follow the instruction and download PoseAug's pretrained model for inference on images/videos.

Citation

If you find this code useful for your research, please consider citing the following paper:

@inproceedings{gong2021poseaug,
  title       = {PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation},
  author      = {Gong, Kehong and Zhang, Jianfeng and Feng, Jiashi},
  booktitle   = {CVPR},
  year        = {2021}
}

Acknowledgements

This code uses SemGCN, SimpleBL, ST-GCN and VPose3D as backbone. We gratefully appreciate the impact these libraries had on our work. If you use our code, please consider citing the original papers as well.