Official code for our CVPR 2021 paper: "When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks".

• Installation

# clone this repo
git clone https://github.com/AIprogrammer/AdvMix
# install dependencies
pip install -r requirements
# make nms
cd AdvMix
cd lib
make
# install cocoapi
# COCOAPI=/path/to/clone/cocoapi
git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
cd $COCOAPI/PythonAPI
# Install into global site-packages
make install
# Alternatively, if you do not have permissions or prefer
# not to install the COCO API into global site-packages
python3 setup.py install --user

• Download the datasets COCO, MPII, and OCHuman. Put them under "./data". The directory structure follows HRNet.

sh scripts/make_datasets.sh

Note: There may be small gap between the results by Evaluation and results in our paper due to randomness of operations in package 'imagecorruptions'.

• MPII

sh scripts/train.sh mpii

• COCO

sh scripts/train.sh coco

sh scripts/test.sh coco
sh scripts/test.sh mpii

Comparisons between standard training and AdvMix on COCO-C. For top-down approaches, results are obtained with detected bounding boxes of HRNet. We see that mPC and rPC are greatly improved, whilst clean performance AP^* can be preserved

Qualitative comparisons between HRNet without and with AdvMix. For each image triplet, the images from left to right are ground truth, predicted results of Standard HRNet-W32, and predicted results of HRNet-W32 with AdvMix.

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

@article{wang2021human,
  title={When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks},
  author={Wang, Jiahang and Jin, Sheng and Liu, Wentao and Liu, Weizhong and Qian, Chen and Luo, Ping},
  journal={arXiv preprint arXiv:2105.06152},
  year={2021}
}

Our research code is released under the MIT license. See LICENSE for details.

Thanks for open-source code HRNet.