Wencan Cheng, Hao Tang, Luc Van Gool and Jong Hwan Ko

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 *Highlight

Our model is trained and tested under:

• Python 3.6.9
• NVIDIA GPU + CUDA CuDNN
• PyTorch (torch == 1.9.0)
• scipy
• tqdm
• Pillow
• yaml
• json
• cv2
• pycocotools

1. Prepare dataset

   please download the NYU Hand dataset

2. Install PointNet++ CUDA operations

   follow the instructions in the './pointnet2' for installation

3. Evaluate

   set the "--test_path" paramter in the test_nyu.sh as the path saved the generated testing set

   execute sh test_nyu.sh

   we provided the pre-trained models ('./results/nyu_handdiff_500iters_com/best_model.pth') for NYU

4. If a new training process is needed, please execute the following instructions after step 1 and 2 are completed

   set the "--dataset_path" paramter in the train_nyu.sh as the path saved the generated traning and testing set respectively

   execute sh train_nyu.sh

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

@inproceedings{cheng2024handdiff,
  title={HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud},
  author={Cheng, Wencan and Tang, Hao and Van Gool, Luc and Ko, Jong Hwan},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition},
  year={2024}
}

We thank repo for the image-point cloud framework.