A minimal solution to hand motion capture from a single color camera at over 100fps. Easy to use, plug to run.

This is the official implementation of the paper "Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data" (CVPR 2020).

This project provides the core components for hand motion capture:

1. estimating joint locations from a monocular RGB image (DetNet)
2. estimating joint rotations from locations (IKNet)

We focus on:

1. ease of use (all you need is a webcam)
2. time efficiency (on our 1080Ti, 8.9ms for DetNet, 0.9ms for IKNet)
3. robustness to occlusion, hand-object interaction, fast motion, changing scale and view point

Some links: [paper] [video] [supp doc] [webpage]

Please check requirements.txt. All dependencies are available via pip and conda.

1. Download MANO model from here and unzip it.
2. In config.py, set OFFICIAL_MANO_PATH to the left hand model.
3. Run python prepare_mano.py, you will get the converted MANO model that is compatible with this project at config.HAND_MESH_MODEL_PATH.

1. Download models from here.
2. Put detnet.ckpt.* in model/detnet, and iknet.ckpt.* in model/iknet.
3. Check config.py, make sure all required files are there.

1. python app.py
2. Put your right hand in front of the camera. The pre-trained model is for left hand, but the input would be flipped internally.
3. Press ESC to quit.

Please check wrappers.py.

If you find the project helpful, please consider citing us:

@inproceedings{zhou2020monocular,
  title={Monocular Real-time Hand Shape and Motion Capture using Multi-modal Data},
  author={Zhou, Yuxiao and Habermann, Marc and Xu, Weipeng and Habibie, Ikhsanul and Theobalt, Christian and Xu, Feng},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={0--0},
  year={2020}
}

We also provide an optimization-based IK solver here.

The detection model is trained with following datasets:

• CMU HandDB
• Rendered Handpose Dataset
• GANerated Hands Dataset

The IK model is trained with the poses shipped with MANO.

Please check our paper about the datasets and training for more details.