EasyMocap
EasyMocap is an open-source toolbox for markerless human motion capture from RGB videos.
In this project, we provide the basic code for fitting SMPL[1]/SMPL+H[2]/SMPLX[3] model to capture body+hand+face poses from multiple views.
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We plan to intergrate more interesting algorithms, please stay tuned!
- [CVPR19] Multi-Person from Multiple Views
- [ECCV20] Mocap from Multiple Uncalibrated and Unsynchronized Videos
- Dense Reconstruction and View Synthesis from Sparse Views
Installation
1. Download SMPL models
This step is the same as smplx.
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.
To download the SMPL+H model go to this project website and register to get access to the downloads section.
To download the SMPL-X model go to this project website and register to get access to the downloads section.
Place them as following:
data
└── smplx
├── J_regressor_body25.npy
├── J_regressor_body25_smplh.txt
├── J_regressor_body25_smplx.txt
├── smpl
│ ├── SMPL_FEMALE.pkl
│ ├── SMPL_MALE.pkl
│ └── SMPL_NEUTRAL.pkl
├── smplh
│ ├── MANO_LEFT.pkl
│ ├── MANO_RIGHT.pkl
│ ├── SMPLH_FEMALE.pkl
│ └── SMPLH_MALE.pkl
└── smplx
├── SMPLX_FEMALE.pkl
├── SMPLX_MALE.pkl
└── SMPLX_NEUTRAL.pkl
2. Requirements
- torch==1.4.0
- torchvision==0.5.0
- opencv-python
- pyrender: for visualization
- chumpy: for loading SMPL model
- OpenPose[4]: for 2D pose
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)], which has 800 frames from 23 synchronized and calibrated cameras. 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
# 2.1 example for SMPL reconstruction
python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --end 300 --vis_smpl --undis --sub_vis 1 7 13 19 --gender male
# 2.2 example for SMPL-X reconstruction
python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --undis --body bodyhandface --sub_vis 1 7 13 19 --start 400 --model smplx --vis_smpl --gender male
# 3.1 example for rendering SMPLX to ${out}/smpl
python3 code/vis_render.py ${data} --out ${out} --skel ${out}/smpl --model smplx --gender male --undis --start 400 --sub_vis 1
# 3.2 example for rendering skeleton of SMPL to ${out}/smplskel
python3 code/vis_render.py ${data} --out ${out} --skel ${out}/smpl --model smplx --gender male --undis --start 400 --sub_vis 1 --type smplskel --body bodyhandface
Not Quick Start
0. Prepare Your Own Dataset
zju-ls-feng
├── intri.yml
├── extri.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> --handface
--openpose: specify the openpose path--handface: detect hands and face keypoints
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
The input flags:
--undis: use to undistort the images--start, --end: control the begin and end number of frames.
The output flags:
--vis_det: visualize the detection--vis_repro: visualize the reprojection--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.
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, 72/78/87)>,
"expression": <(1, 10)>,
"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.
If you use SMPL+H model, the poses contains 22x3+6+6. We use 6 pca coefficients for each hand. 3(jaw, left eye, right eye)x3 poses of head are added for SMPL-X model.
Evaluation
In our code, we do not set the best weight parameters, you can adjust these according your data. If you find a set of good weights, feel free to tell me.
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={Peng, Sida and Zhang, Yuanqing and Xu, Yinghao and Wang, Qianqian and Shuai, Qing and Bao, Hujun and Zhou, Xiaowei},
journal={arXiv preprint arXiv:2012.15838},
year={2020}
}
Reference
[1] Loper, Matthew, et al. "SMPL: A skinned multi-person linear model." ACM transactions on graphics (TOG) 34.6 (2015): 1-16.
[2] Romero, Javier, Dimitrios Tzionas, and Michael J. Black. "Embodied hands: Modeling and capturing hands and bodies together." ACM Transactions on Graphics (ToG) 36.6 (2017): 1-17.
[3] Pavlakos, Georgios, et al. "Expressive body capture: 3d hands, face, and body from a single image." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
Bogo, Federica, et al. "Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image." European conference on computer vision. Springer, Cham, 2016.
[4] Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: Openpose: real-time multi-person 2d pose estimation using part affinity fields. arXiv preprint arXiv:1812.08008 (2018)
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