170 lines
6.6 KiB
Markdown
170 lines
6.6 KiB
Markdown
<!--
|
||
* @Date: 2021-01-13 20:32:12
|
||
* @Author: Qing Shuai
|
||
* @LastEditors: Qing Shuai
|
||
* @LastEditTime: 2021-01-14 21:43:44
|
||
* @FilePath: /EasyMocapRelease/Readme.md
|
||
-->
|
||
# EasyMocap
|
||
**EasyMocap** is an open-source toolbox for **markerless human motion capture**.
|
||
|
||
## Results
|
||
|:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL|
|
||
|----|----|
|
||
|||
|
||
|
||
> 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={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}
|
||
}
|
||
```
|
||
|
||
<!-- ## License -->
|