EasyMocap/Readme.md

145 lines
5.4 KiB
Markdown
Raw Normal View History

2021-01-14 21:17:40 +08:00
<!--
* @Date: 2021-01-13 20:32:12
* @Author: Qing Shuai
* @LastEditors: Qing Shuai
2021-01-14 21:32:09 +08:00
* @LastEditTime: 2021-01-14 21:31:39
2021-01-14 21:22:44 +08:00
* @FilePath: /EasyMocapRelease/Readme.md
2021-01-14 21:17:40 +08:00
-->
# EasyMocap
**EasyMocap** is an open-source toolbox for **markerless human motion capture**.
## Results
|:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL|
|----|----|
2021-01-14 21:22:44 +08:00
|![repro](doc/feng/repro_512.gif)|![smpl](doc/feng/smpl_512.gif)|
2021-01-14 21:17:40 +08:00
|:black_square_button: Whole Body|:black_square_button: [Detailed Mesh](https://zju3dv.github.io/neuralbody/)|
|----|----|
2021-01-14 21:25:56 +08:00
|<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>|
2021-01-14 21:17:40 +08:00
## 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
```
<!-- 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
2021-01-14 21:22:44 +08:00
We provide an example multiview dataset[[dropbox]()][[BaiduDisk]()]. After downloading the dataset, you can run the following example scripts.
2021-01-14 21:17:40 +08:00
```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
2021-01-14 21:32:09 +08:00
data=path/to/data
out=path/to/output
2021-01-14 21:17:40 +08:00
python3 scripts/preprocess/extract_video.py ${data} --openpose <openpose_path>
```
### 2. Run the code
2021-01-14 21:32:09 +08:00
```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.
2021-01-14 21:17:40 +08:00
### 3. Output
The results are saved in `json` format.
```bash
2021-01-14 21:32:09 +08:00
<output_root>
2021-01-14 21:17:40 +08:00
├── 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 some great resources we benefit:
- SMPL models and layer is borrowed 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)
We also would like to thank Wenduo Feng for the example data.
## Contact
Please open an issue if you have any questions.
## Citation
This project is the base of our other works: [iMocap](https://zju3dv.github.io/iMoCap/), [Neural Body](https://zju3dv.github.io/neuralbody/)
```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}
}
```
<!-- ## License -->