# EasyMocap **EasyMocap** is an open-source toolbox for **markerless human motion capture** from RGB videos. In this project, we provide the basic code for fitting SMPL[1]/SMPL+H[2]/SMPLX[3] model to capture body+hand+face poses from multiple views. |Input|:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL| |----|----|----| |||| > We plan to intergrate more interesting algorithms, please stay tuned! 1. [Multi-Person from Multiple Views](https://github.com/zju3dv/mvpose) 2. [Mocap from Multiple **Uncalibrated** and **Unsynchronized** Videos](https://arxiv.org/pdf/2008.07931.pdf) 3. [Dense Reconstruction and View Synthesis from **Sparse Views**](https://zju3dv.github.io/neuralbody/) ## Installation ### 1. Download SMPL models This step is the same as [smplx](https://github.com/vchoutas/smplx#model-loading). 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. 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. **Place them as following:** ```bash data └── smplx ├── J_regressor_body25.npy ├── J_regressor_body25_smplh.txt ├── J_regressor_body25_smplx.txt ├── smpl │   ├── SMPL_FEMALE.pkl │   ├── SMPL_MALE.pkl │   └── SMPL_NEUTRAL.pkl ├── smplh │   ├── MANO_LEFT.pkl │   ├── MANO_RIGHT.pkl │   ├── SMPLH_female.pkl │   ├── SMPLH_FEMALE.pkl │   ├── SMPLH_male.pkl │   └── SMPLH_MALE.pkl └── smplx ├── SMPLX_FEMALE.pkl ├── SMPLX_MALE.pkl └── SMPLX_NEUTRAL.pkl ``` ### 2. Requirements - torch==1.4.0 - torchvision==0.5.0 - opencv-python - [pyrender](https://pyrender.readthedocs.io/en/latest/install/index.html#python-installation): for visualization - chumpy: for loading SMPL model - OpenPose[4]: for 2D pose 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)], which has 800 frames from 23 synchronized and calibrated cameras. 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 # 2.1 example for SMPL reconstruction python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --end 300 --vis_smpl --undis --sub_vis 1 7 13 19 --gender male # 2.2 example for SMPL-X reconstruction python3 code/demo_mv1pmf_smpl.py ${data} --out ${out} --undis --body bodyhandface --sub_vis 1 7 13 19 --start 400 --model smplx --vis_smpl --gender male # 3.1 example for rendering SMPLX to ${out}/smpl python3 code/vis_render.py ${data} --out ${out} --skel ${out}/smpl --model smplx --gender male --undis --start 400 --sub_vis 1 # 3.2 example for rendering skeleton of SMPL to ${out}/smplskel python3 code/vis_render.py ${data} --out ${out} --skel ${out}/smpl --model smplx --gender male --undis --start 400 --sub_vis 1 --type smplskel --body bodyhandface ``` ## Not Quick Start ### 0. Prepare Your Own Dataset ```bash zju-ls-feng ├── intri.yml ├── extri.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 --handface ``` - `--openpose`: specify the openpose path - `--handface`: detect hands and face keypoints ### 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 ``` The input flags: - `--undis`: use to undistort the images - `--start, --end`: control the begin and end number of frames. The output flags: - `--vis_det`: visualize the detection - `--vis_repro`: visualize the reprojection - `--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. ### 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, 72/78/87)>, "expression": <(1, 10)>, "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. If you use SMPL+H model, the poses contains `22x3+6+6`. We use `6` pca coefficients for each hand. `3(jaw, left eye, right eye)x3` poses of head are added for SMPL-X model. ## Evaluation In our code, we do not set the best weight parameters, you can adjust these according your data. If you find a set of good weights, feel free to tell me. We will add more quantitative reports in [doc/evaluation.md](doc/evaluation.md) ## 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} } ``` ## Reference ```bash [1] Loper, Matthew, et al. "SMPL: A skinned multi-person linear model." ACM transactions on graphics (TOG) 34.6 (2015): 1-16. [2] Romero, Javier, Dimitrios Tzionas, and Michael J. Black. "Embodied hands: Modeling and capturing hands and bodies together." ACM Transactions on Graphics (ToG) 36.6 (2017): 1-17. [3] Pavlakos, Georgios, et al. "Expressive body capture: 3d hands, face, and body from a single image." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. Bogo, Federica, et al. "Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image." European conference on computer vision. Springer, Cham, 2016. [4] Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: Openpose: real-time multi-person 2d pose estimation using part affinity fields. arXiv preprint arXiv:1812.08008 (2018) ```