[ICCV-2023] Source-free Domain Adaptive Human Pose Estimation

Packages Prerequisites:

python == 3.6.8
pytorch ==1.1.0
torchvision == 0.3.0
numpy, scipy, sklearn, PIL, argparse, tqdm

Datasets Preparations:

Please follow the instructions from RegDA

Training:

python train_sfda.py ../RegDA_tokenpose/data/RHD ../RegDA_tokenpose/data/H3D_crop -s RenderedHandPose -t Hand3DStudio --target-train Hand3DStudio_mt --log logs/r2h_exp/syn2real --debug --seed 0 --lambda_c 1 --pretrain-epoch 40 --rotation_stu 180 --shear_stu -30 30 --translate_stu 0.05 0.05 --scale_stu 0.6 1.3 --color_stu 0.25 --blur_stu 0 --rotation_tea 180 --shear_tea -30 30 --translate_tea 0.05 0.05 --scale_tea 0.6 1.3 --color_tea 0.25 --blur_tea 0 -b 32 --mask-ratio 0.5 --k 1 --occlude-rate 0.5 --occlude-thresh 0.9

Citation

If you find this code useful for your research, please cite our paper

@article{peng2023source,
  title={Source-free Domain Adaptive Human Pose Estimation},
  author={Peng, Qucheng and Zheng, Ce and Chen, Chen},
  journal={arXiv preprint arXiv:2308.03202},
  year={2023}
}

Acknowledge

Borrow a lot from RegDA and UniFrame.

@InProceedings{RegDA,
    author    = {Junguang Jiang and
                Yifei Ji and
                Ximei Wang and
                Yufeng Liu and
                Jianmin Wang and
                Mingsheng Long},
    title     = {Regressive Domain Adaptation for Unsupervised Keypoint Detection},
    booktitle = {CVPR},
    year = {2021}
}

@InProceedings{kim2022unified,
  title={A Unified Framework for Domain Adaptive Pose Estimation},
  author={Kim, Donghyun and Wang, Kaihong and Saenko, Kate and Betke, Margrit and Sclaroff, Stan},
  booktitle = {The European Conference on Computer Vision (ECCV)},
  year = {2022} 
 }

davidpengucf / SFDAHPE