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* @Date: 2021-01-13 20:32:12 * @Date: 2021-01-13 20:32:12
* @Author: Qing Shuai * @Author: Qing Shuai
* @LastEditors: Qing Shuai * @LastEditors: Qing Shuai
* @LastEditTime: 2021-01-14 21:43:44 * @LastEditTime: 2021-01-17 21:07:07
* @FilePath: /EasyMocapRelease/Readme.md * @FilePath: /EasyMocapRelease/Readme.md
--> -->
# EasyMocap # EasyMocap
**EasyMocap** is an open-source toolbox for **markerless human motion capture**. **EasyMocap** is an open-source toolbox for **markerless human motion capture** from RGB videos.
## Features
- [x] multi-view, single person => 3d body keypoints
- [x] multi-view, single person => SMPL parameters
## Results
|:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL| |:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL|
|----|----| |----|----|
|![repro](doc/feng/repro_512.gif)|![smpl](doc/feng/smpl_512.gif)| |![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! > 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 |Input|Output|
- [ ] Dense reconstruction and view synthesis from sparse view: [Neural Body](https://zju3dv.github.io/neuralbody/). |----|----|
|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](https://zju3dv.github.io/neuralbody/).|
|:black_square_button: Whole Body|:black_square_button: [Detailed Mesh](https://zju3dv.github.io/neuralbody/)| |:black_square_button: Whole Body|:black_square_button: [Detailed Mesh](https://zju3dv.github.io/neuralbody/)|
|----|----| |----|----|
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## Installation ## Installation
### 1. Download SMPL models ### 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:** To download the *SMPL* model go to [this](http://smpl.is.tue.mpg.de) (male and female models, version 1.0.0, 10 shape PCs) and [this](http://smplify.is.tue.mpg.de) (gender neutral model) project website and register to get access to the downloads section. Prepare the model as [smplx](https://github.com/vchoutas/smplx#model-loading). **Place them as following:**
```bash ```bash
data data
└── smplx └── smplx
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- torch==1.4.0 - torch==1.4.0
- torchvision==0.5.0 - torchvision==0.5.0
- opencv-python - opencv-python
- pyrender: for visualization - [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html#python-installation): for visualization
- chumpy: for loading SMPL model - chumpy: for loading SMPL model
Some of python libraries can be found in `requirements.txt`. You can test different version of PyTorch. Some of python libraries can be found in `requirements.txt`. You can test different version of PyTorch.
@ -131,6 +137,10 @@ The data in `smpl/000000.json` is also a list, each element represents the SMPL
``` ```
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. 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](doc/evaluation.md)
## Acknowledgements ## Acknowledgements
Here are the great works this project is built upon: Here are the great works this project is built upon:
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journal={arXiv preprint arXiv:2012.15838}, journal={arXiv preprint arXiv:2012.15838},
year={2020} year={2020}
} }
``` ```
<!-- ## License -->