README.md

Unsupervised Domain Adaptation for Keypoint Detection

Dataset

Following datasets can be downloaded automatically:

• Rendered Handpose Dataset
• Hand-3d-Studio Dataset
• FreiHAND Dataset
• Surreal Dataset
• LSP Dataset

You need to prepare following datasets manually if you want to use them:

• Human3.6M Dataset

and prepare them following Documentations for Human3.6M Dataset.

Supported Methods

Supported methods include:

• Regressive Domain Adaptation for Unsupervised Keypoint Detection (RegDA, CVPR 2021)

Experiment and Results

The shell files give the script to reproduce the benchmarks with specified hyper-parameters. For example, if you want to train DANN on Office31, use the following script

# Train a RegDA on RHD -> H3D task using PoseResNet.
# Assume you have put the datasets under the path `data/RHD` and  `data/H3D_crop`, 
# or you are glad to download the datasets automatically from the Internet to this path
CUDA_VISIBLE_DEVICES=0 python regda.py data/RHD data/H3D_crop \
    -s RenderedHandPose -t Hand3DStudio --finetune --seed 0 --debug --log logs/regda/rhd2h3d

For more information please refer to Get Started for help.

TODO

Support methods: CycleGAN

Citation

If you use these methods in your research, please consider citing.

@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}
}