# 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/)| |----|----| |

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| ## 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. ## 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 ``` ### 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 ├── 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': , '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": , "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} } ```