Awesome

A2J-Transformer

Introduction

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

About our code

Updates

(2023-9-19) Training code released! All training codes for Interhand 2.6M, NYU and HANDS 2017 dataset.
(2023-9-19) Updated test.py and base.py, added the argparse test_epoch. Updated the name of our pre-trained model, from snapshot.pth.tar to snapshot_0.pth.tar.
(2023-4-23) Deleted some compiled files in the dab_deformable_detr\ops folder.

Installation and Setup

Requirements

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

Compiling CUDA operators(Following Deformable-DETR)

cd ./dab_deformable_detr/ops
sh make.sh

Usage

Dataset preparation

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

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

Testing on InterHand 2.6M Dataset

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_0.pth.tar

The datalist folder and the pkl files denotes the dataset-list generated during running the code.

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.

NYU and HANDS 2017 dataset

Please use our code following A2J to prepare the datasets.
If you have prepared, just run the nyu.py and hands2017.py.

Cite

Our code is protected by patents and cannot be used for commercial purposes. If you have commercial needs, please contact Prof. Yang Xiao (Huazhong University of Science and Technology): Yang_Xiao@hust.edu.cn.

If you find our work useful in your research or publication, please cite our work:

@inproceedings{jiang2023a2j,
  title={A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image},
  author={Jiang, Changlong and Xiao, Yang and Wu, Cunlin and Zhang, Mingyang and Zheng, Jinghong and Cao, Zhiguo and Zhou, Joey Tianyi},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8846--8855},
  year={2023}
}