EasyMocap

EasyMocap is an open-source toolbox for markerless human motion capture from RGB videos.

Features

• multi-view, single person => 3d body keypoints
• multi-view, single person => SMPL parameters

✔️ Skeleton	✔️ SMPL

The following features are not released yet. We are now working hard on them. Please stay tuned!

Input	Output
multi-view, single person	whole body 3d keypoints
multi-view, single person	SMPL-H/SMPLX/MANO parameters
sparse view, single person	dense reconstruction and view synthesis: NeuralBody.

🔲 Whole Body	🔲 Detailed Mesh

Installation

1. Download SMPL models

To download the SMPL model go to this (male and female models, version 1.0.0, 10 shape PCs) and this (gender neutral model) project website and register to get access to the downloads section. Prepare the model as smplx. Place them as following:

data
└── smplx
    ├── J_regressor_body25.npy
    └── smpl
        ├── SMPL_FEMALE.pkl
        ├── SMPL_MALE.pkl
        └── SMPL_NEUTRAL.pkl

2. Requirements

• torch==1.4.0
• torchvision==0.5.0
• opencv-python
• pyrender: for visualization
• chumpy: for loading SMPL model

Some of python libraries can be found in requirements.txt. You can test different version of PyTorch.

Quick Start

We provide an example multiview dataset[dropbox][BaiduDisk(vg1z)]. After downloading the dataset, you can run the following example scripts.

data=path/to/data
out=path/to/output
# 0. extract the video to images
python3 scripts/preprocess/extract_video.py ${data}
# 1. example for skeleton reconstruction
python3 code/demo_mv1pmf_skel.py ${data} --out ${out} --vis_det --vis_repro --undis --sub_vis 1 7 13 19
# 2. example for SMPL reconstruction
python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --end 300 --vis_smpl --undis --sub_vis 1 7 13 19

Not Quick Start

0. Prepare Your Own Dataset

zju-ls-feng
├── extri.yml
├── intri.yml
└── videos
    ├── 1.mp4
    ├── 2.mp4
    ├── ...
    ├── 8.mp4
    └── 9.mp4

The input videos are placed in videos/.

Here intri.yml and extri.yml store the camera intrinsici and extrinsic parameters. For example, if the name of a video is 1.mp4, then there must exist K_1, dist_1 in intri.yml, and R_1((3, 1), rotation vector of camera), T_1(3, 1) in extri.yml. The file format is following OpenCV format.

1. Run OpenPose

data=path/to/data
out=path/to/output
python3 scripts/preprocess/extract_video.py ${data} --openpose <openpose_path> 

2. Run the code

# 1. example for skeleton reconstruction
python3 code/demo_mv1pmf_skel.py ${data} --out ${out} --vis_det --vis_repro --undis --sub_vis 1 7 13 19
# 2. example for SMPL reconstruction
python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --end 300 --vis_smpl --undis --sub_vis 1 7 13 19

• --vis_det: visualize the detection
• --vis_repro: visualize the reprojection
• --undis: use to undistort the images
• --sub_vis: use to specify the views to visualize. If not set, the code will use all views
• --vis_smpl: use to render the SMPL mesh to images.
• --start, --end: control the begin and end number of frames.

3. Output

The results are saved in json format.

<output_root>
├── keypoints3d
│   ├── 000000.json
│   └── xxxxxx.json
└── smpl
    ├── 000000.jpg
    ├── 000000.json
    └── 000004.json

The data in keypoints3d/000000.json is a list, each element represents a human body.

{
    'id': <id>,
    'keypoints3d': [[x0, y0, z0, c0], [x1, y1, z0, c1], ..., [xn, yn, zn, cn]]
}

The data in smpl/000000.json is also a list, each element represents the SMPL parameters which is slightly different from official model.

{
    "id": <id>,
    "Rh": <(1, 3)>,
    "Th": <(1, 3)>,
    "poses": <(1, 72)>,
    "shapes": <(1, 10)>
}

We set the first 3 dimensions of poses to zero, and add a new parameter Rh to represents the global oritentation, the vertices of SMPL model V = RX(theta, beta) + T.

Evaluation

We will add more quantitative reports in doc/evaluation.md

Acknowledgements

Here are the great works this project is built upon:

• SMPL models and layer are from MPII SMPL-X model.
• Some functions are borrowed from SPIN, VIBE, SMPLify-X
• The method for fitting 3D skeleton and SMPL model is similar to TotalCapture, without using point cloud.

We also would like to thank Wenduo Feng who is the performer in the sample data.

Contact

Please open an issue if you have any questions.

Citation

This project is a part of our work iMocap and Neural Body

Please consider citing these works if you find this repo is useful for your projects.

@inproceedings{dong2020motion,
  title={Motion capture from internet videos},
  author={Dong, Junting and Shuai, Qing and Zhang, Yuanqing and Liu, Xian and Zhou, Xiaowei and Bao, Hujun},
  booktitle={European Conference on Computer Vision},
  pages={210--227},
  year={2020},
  organization={Springer}
}

@article{peng2020neural,
  title={Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans},
  author={Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, Xiaowei Zhou},
  journal={arXiv preprint arXiv:2012.15838},
  year={2020}
}