SMPL-Fitting

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Fitting SMPL Parameters to 3D Keypoints

SMPL-Fitting 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Fitting SMPL Parameters to 3D Keypoints

![teaser](https://raw.githubusercontent.com/yc4ny/SMPL-Fitting/main/assets/teaser.png)

## Installation
First you need to clone the repo:
```
git clone https://github.com/yc4ny/SMPL-Fitting.git
cd SMPL-Fitting
```
This code has been tested on **Python 3.7**, **Pytorch 1.7.1**, **CUDA 11.0**.
We recommend creating a virtual environment for this repository. You can use conda:
```
conda create -n smplfitting python==3.7
```

Then, you need to install Pytorch according to your CUDA version and GPU requriements. This is for CUDA 11.0, but you can adapt accordingly: 
```
pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
```

Then, you can install the rest of the dependencies with: 
```
pip install -r requirements.txt
```

## Preprocessing Files

#### 1. Prepare **.npy** files containing the 3D Keypoint Data
You must preprocess your data to match the input format. 
The code will take a **.npy** file as input containing an array of shape $(N \times J \times 3)$, where $N$ is the number of frames in the sequence, $J$ the number of joints in the 3D Keypoint data. For example, if there is a $(500 \times 24 \times 3)$ array given as input, in the data there are $24 \times 3D$ Joints per frame and $500$ frames in total. Put all the processed **.npy** files in a single folder, it will be used for the **--dataset_dir** flag in the demo.

#### 2. Create a configs.json file 
You need to have a configs.json file. There is ```configs/3DPW.json``` file that I have used for fitting the 3DPW dataset. Make a copy of the file, change the name ```NAME``` and ```PATH``` according to your data. Make sure to change the ```DATA_MAP``` for mapping index of the joints of the data you wish to fit with the original SMPL keypoints.

## Demo
To perform fitting, you can run:
```bash
python fit.py --dataset_name {name of the data} --dataset_dir {directory of the preprocessed .npy files}
```

## Todo
- [X] Add 3DPW Dataset Fitting
- [ ] Add MANO for accurate hand fitting

## Citation
```
@inproceedings{SMPL-X:2019,
    title = {Expressive Body Capture: 3D Hands, Face, and Body from a Single Image},
    author = {Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.},
    booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
    year = {2019}
}
```

```
@article{SMPL:2015,
    author = {Loper, Matthew and Mahmood, Naureen and Romero, Javier and Pons-Moll, Gerard and Black, Michael J.},
    title = {{SMPL}: A Skinned Multi-Person Linear Model},
    journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH Asia)},
    month = oct,
    number = {6},
    pages = {248:1--248:16},
    publisher = {ACM},
    volume = {34},
    year = {2015}
}
```

## Acknowledgements

Parts of the code are taken or adapted from the following repos:

- [SMPLPytorch](https://github.com/gulvarol/smplpytorch)
- [MultiviewSMPLifyX](https://github.com/ZhengZerong/MultiviewSMPLifyX)
- [Smplify-x](https://github.com/vchoutas/smplify-x)
- [Pose2SMPL](https://github.com/Dou-Yiming/Pose_to_SMPL)

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