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HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud

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

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

Prerequisities

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}
}

Acknowledgement

We thank repo for the image-point cloud framework.