168 lines
6.3 KiB
Markdown
168 lines
6.3 KiB
Markdown
<!--
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* @Date: 2021-01-13 20:32:12
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* @Author: Qing Shuai
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* @LastEditors: Qing Shuai
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* @LastEditTime: 2021-01-14 21:43:44
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* @FilePath: /EasyMocapRelease/Readme.md
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-->
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# EasyMocap
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**EasyMocap** is an open-source toolbox for **markerless human motion capture**.
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## Results
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|:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL|
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|----|----|
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> The following codes are not released. We are now working hard on them.
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- [ ] Whole body 3d keypoints estimation
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- [ ] SMPL-H/SMPLX support
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- [ ] Detailed mesh from sparse view
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|:black_square_button: Whole Body|:black_square_button: [Detailed Mesh](https://zju3dv.github.io/neuralbody/)|
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|----|----|
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|<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>|
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## Installation
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### 1. Download SMPL models
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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:**
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```bash
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data
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└── smplx
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├── J_regressor_body25.npy
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└── smpl
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├── SMPL_FEMALE.pkl
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├── SMPL_MALE.pkl
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└── SMPL_NEUTRAL.pkl
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```
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### 2. Requirements
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- torch==1.4.0
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- torchvision==0.5.0
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- opencv-python
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- pyrender: for visualization
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- chumpy: for loading SMPL model
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Some of python libraries can be found in `requirements.txt`. You can test different version of PyTorch.
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<!-- 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.
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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. -->
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## Quick Start
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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.
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```bash
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data=path/to/data
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out=path/to/output
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# 0. extract the video to images
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python3 scripts/preprocess/extract_video.py ${data}
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# 1. example for skeleton reconstruction
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python3 code/demo_mv1pmf_skel.py ${data} --out ${out} --vis_det --vis_repro --undis --sub_vis 1 7 13 19
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# 2. example for SMPL reconstruction
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python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --end 300 --vis_smpl --undis --sub_vis 1 7 13 19
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```
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## Not Quick Start
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### 0. Prepare Your Own Dataset
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```bash
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zju-ls-feng
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├── extri.yml
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├── intri.yml
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└── videos
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├── 1.mp4
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├── 2.mp4
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├── ...
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├── 8.mp4
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└── 9.mp4
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```
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The input videos are placed in `videos/`.
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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).
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### 1. Run [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose)
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```bash
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data=path/to/data
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out=path/to/output
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python3 scripts/preprocess/extract_video.py ${data} --openpose <openpose_path>
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```
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### 2. Run the code
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```bash
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# 1. example for skeleton reconstruction
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python3 code/demo_mv1pmf_skel.py ${data} --out ${out} --vis_det --vis_repro --undis --sub_vis 1 7 13 19
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# 2. example for SMPL reconstruction
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python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --end 300 --vis_smpl --undis --sub_vis 1 7 13 19
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```
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- `--vis_det`: visualize the detection
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- `--vis_repro`: visualize the reprojection
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- `--undis`: use to undistort the images
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- `--sub_vis`: use to specify the views to visualize. If not set, the code will use all views
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- `--vis_smpl`: use to render the SMPL mesh to images.
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- `--start, --end`: control the begin and end number of frames.
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### 3. Output
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The results are saved in `json` format.
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```bash
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<output_root>
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├── keypoints3d
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│ ├── 000000.json
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│ └── xxxxxx.json
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└── smpl
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├── 000000.jpg
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├── 000000.json
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└── 000004.json
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```
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The data in `keypoints3d/000000.json` is a list, each element represents a human body.
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```bash
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{
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'id': <id>,
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'keypoints3d': [[x0, y0, z0, c0], [x1, y1, z0, c1], ..., [xn, yn, zn, cn]]
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}
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```
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The data in `smpl/000000.json` is also a list, each element represents the SMPL parameters which is slightly different from official model.
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```bash
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{
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"id": <id>,
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"Rh": <(1, 3)>,
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"Th": <(1, 3)>,
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"poses": <(1, 72)>,
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"shapes": <(1, 10)>
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}
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```
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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.
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## Acknowledgements
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Here are some great resources we benefit:
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- SMPL models and layer is borrowed from MPII [SMPL-X model](https://github.com/vchoutas/smplx).
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- 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)
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- Our project is similar with [TotalCapture](http://www.cs.cmu.edu/~hanbyulj/totalcapture/)
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We also would like to thank Wenduo Feng for the example data.
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## Contact
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Please open an issue if you have any questions.
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## Citation
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This project is the base of our other works: [iMocap](https://zju3dv.github.io/iMoCap/), [Neural Body](https://zju3dv.github.io/neuralbody/)
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```bibtex
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@inproceedings{dong2020motion,
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title={Motion capture from internet videos},
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author={Dong, Junting and Shuai, Qing and Zhang, Yuanqing and Liu, Xian and Zhou, Xiaowei and Bao, Hujun},
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booktitle={European Conference on Computer Vision},
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pages={210--227},
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year={2020},
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organization={Springer}
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}
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@article{peng2020neural,
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title={Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans},
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author={Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, Xiaowei Zhou},
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journal={arXiv preprint arXiv:2012.15838},
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year={2020}
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}
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```
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<!-- ## License -->
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