EasyMocap/Readme.md

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 * @Date: 2021-01-13 20:32:12
 * @Author: Qing Shuai
 * @LastEditors: Qing Shuai
 * @LastEditTime: 2021-01-14 21:43:44
 * @FilePath: /EasyMocapRelease/Readme.md
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# EasyMocap
**EasyMocap** is an open-source toolbox for **markerless human motion capture**.

## Results
|:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL|
|----|----|
|![repro](doc/feng/repro_512.gif)|![smpl](doc/feng/smpl_512.gif)|

> The following features are not released yet. We are now working hard on them. Please stay tuned!
- [ ] Whole body 3d keypoints estimation
- [ ] SMPL-H/SMPLX support
- [ ] Dense reconstruction and view synthesis from sparse view: [Neural Body](https://zju3dv.github.io/neuralbody/).

|:black_square_button: Whole Body|:black_square_button: [Detailed Mesh](https://zju3dv.github.io/neuralbody/)|
|----|----|
|<div align="center"><img src="doc/feng/total_512.gif" height="300" alt="mesh" align=center /></div>|<div align="center"><img src="doc/feng/body_256.gif" height="300" width="300" alt="mesh" align=center /></div>|

## Installation
### 1. Download SMPL models
To download the *SMPL* model go to [this](http://smpl.is.tue.mpg.de) (male and female models) and [this](http://smplify.is.tue.mpg.de) (gender neutral model) project website and register to get access to the downloads section. **Place them as following:**
```bash
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.

<!-- To download the *SMPL+H* model go to [this project website](http://mano.is.tue.mpg.de) and register to get access to the downloads section. 

To download the *SMPL-X* model go to [this project website](https://smpl-x.is.tue.mpg.de) and register to get access to the downloads section.  -->

## Quick Start
We provide an example multiview dataset[[dropbox](https://www.dropbox.com/s/24mb7r921b1g9a7/zju-ls-feng.zip?dl=0)][[BaiduDisk](https://pan.baidu.com/s/1lvAopzYGCic3nauoQXjbPw)(vg1z)]. After downloading the dataset, you can run the following example scripts.
```bash
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
```bash
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](https://docs.opencv.org/master/dd/d74/tutorial_file_input_output_with_xml_yml.html).

### 1. Run [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)
```bash
data=path/to/data
out=path/to/output
python3 scripts/preprocess/extract_video.py ${data} --openpose <openpose_path> 
```

### 2. Run the code
```bash
# 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.
```bash
<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.
```bash
{
    '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.
```bash
{
    "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.

## Acknowledgements
Here are the great works this project is built upon:

- SMPL models and layer are from MPII [SMPL-X model](https://github.com/vchoutas/smplx).
- Some functions are borrowed from [SPIN](https://github.com/nkolot/SPIN), [VIBE](https://github.com/mkocabas/VIBE), [SMPLify-X](https://github.com/vchoutas/smplify-x)
- The method for fitting 3D skeleton and SMPL model is similar to [TotalCapture](http://www.cs.cmu.edu/~hanbyulj/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](https://zju3dv.github.io/iMoCap/) and [Neural Body](https://zju3dv.github.io/neuralbody/)

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

```bibtex
@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}
}
```

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