This is the official implementation for the paper, "A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image", CVPR 2023.

Paper link here: A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image

• Our code is tested under Ubuntu 20.04 environment with NVIDIA 2080Ti GPU and NVIDIA 3090 GPU, both Pytorch1.7 and Pytorch1.11 work.

• Python>=3.7

  We recommend you to use Anaconda to create a conda environment:

  conda create --name a2j_trans python=3.7

  Then, activate the environment:

  conda activate a2j_trans

• PyTorch>=1.7.1, torchvision>=0.8.2 (following instructions here)

  We recommend you to use the following pytorch and torchvision:

  conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch

• Other requirements

  conda install tqdm numpy matplotlib scipy
  pip install opencv-python pycocotools

cd ./dab_deformable_detr/ops
sh make.sh

• Please download InterHand 2.6M Dataset and organize them as following:

  your_dataset_path/
  └── Interhand2.6M_5fps/
      ├── annotations/
      └── images/
  

• Please download our pre-trained model and organize the code as following:

  a2j-transformer/
  ├── dab_deformable_detr/
  ├── nets/
  ├── utils/
  ├── ...py
  ├── datalist/
  |   └── ...pkl
  └── output/
      └── model_dump/
          └── snapshot.pth.tar
  

  The datalist folder and the pkl files denotes the dataset-list generated during running the code. You can choose to download them here, and manually put them under the datalist folder.

• In config.py, set interhand_anno_dir, interhand_images_path to the dataset abs directory.

• In config.py, set cur_dir to the a2j-transformer code directory.

• Run the following script:

  python test.py --gpu <your_gpu_ids>

  You can also choose to change the gpu_ids in test.py.