A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator

FrankMocap

FrankMocap: A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator

FrankMocap pursues an easy-to-use single view 3D motion capture system developed by Facebook AI Research (FAIR). FrankMocap provides state-of-the-art 3D pose estimation outputs for body, hand, and body+hands in a single system. The core objective of FrankMocap is to democratize the 3D human pose estimation technology, enabling anyone (researchers, engineers, developers, artists, and others) can easily obtain 3D motion capture outputs from videos and images.

Key Features

Body Motion Capture:

Hand Motion Capture

Egocentric Hand Motion Capture

Whole body Motion Capture (body + hands)

A Quick Start

Run body motion capture

# using a machine with a monitor to show output on screen
python -m demo.demo_bodymocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output

# screenless mode (e.g., a remote server)
xvfb-run -a python -m demo.demo_bodymocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output

Run hand motion capture

# using a machine with a monitor to show outputs on screen
python -m demo.demo_handmocap --input_path ./sample_data/han_hand_short.mp4 --out_dir ./mocap_output

# screenless mode  (e.g., a remote server)
xvfb-run -a python -m demo.demo_handmocap --input_path ./sample_data/han_hand_short.mp4 --out_dir ./mocap_output

Run whole body motion capture

# using a machine with a monitor to show outputs on screen
python -m demo.demo_frankmocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output

# screenless mode  (e.g., a remote server)
xvfb-run -a python -m demo.demo_frankmocap --input_path ./sample_data/han_short.mp4 --out_dir ./mocap_output

Note:
- Above commands use openGL by default. If it does not work, you may try alternative renderers (pytorch3d or openDR).
- See the readme of each module for details

References

FrankMocap is based on the following research outputs:

@InProceedings{rong2021frankmocap,
  title={FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration},
  author={Rong, Yu and Shiratori, Takaaki and Joo, Hanbyul},
  booktitle={IEEE International Conference on Computer Vision Workshops},
  year={2021}
}

@article{joo2020eft,
  title={Exemplar Fine-Tuning for 3D Human Pose Fitting Towards In-the-Wild 3D Human Pose Estimation},
  author={Joo, Hanbyul and Neverova, Natalia and Vedaldi, Andrea},
  journal={arXiv preprint arXiv:2004.03686},
  year={2020}
}