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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: 2021-03-13 21:52:17
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* @LastEditTime: 2021-04-02 12:26:56
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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** from RGB videos.
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**EasyMocap** is an open-source toolbox for **markerless human motion capture** from RGB videos. In this project, we provide a lot of motion capture demos in different settings.
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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.
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|Input(23 views)|:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL|
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|----|----|----|
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||||
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----
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> We plan to intergrate more interesting algorithms, please stay tuned!
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## Core features
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1. [[CVPR19] Multi-Person from Multiple Views](https://github.com/zju3dv/mvpose)
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2. [[ECCV20] Mocap from Multiple **Uncalibrated** and **Unsynchronized** Videos](https://arxiv.org/pdf/2008.07931.pdf)
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3. [[CVPR21] Dense Reconstruction and View Synthesis from **Sparse Views**](https://zju3dv.github.io/neuralbody/)
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4. [[CVPR21] Reconstructing 3D Human Pose by Watching Humans in the **Mirror**](https://zju3dv.github.io/Mirrored-Human/)
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<table border="0">
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<tr>
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<td><img src="./doc/mirror/image.jpg" height = "150" alt="图片名称" align=center /></td>
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<td><img src="./doc/mirror/scene_cover_green.png" height = "200" alt="图片名称" align=center /></td>
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</tr>
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</table>
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### Multiple views of single person
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This is the basic code for fitting SMPL[1]/SMPL+H[2]/SMPL-X[3] model to capture body+hand+face poses from multiple views.
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<div align="center">
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<img src="doc/feng/mv1pmf-smplx.gif" width="80%">
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<br>
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<sup>Videos are from ZJU-MoCap, with 23 calibrated and synchronized cameras.<sup/>
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</div>
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### Internet video with a mirror
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[](https://arxiv.org/pdf/2104.00340.pdf)
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<div align="center">
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<img src="https://raw.githubusercontent.com/zju3dv/Mirrored-Human/main/doc/assets/smpl-avatar.gif" width="80%">
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<br>
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<sup>This video is from <a href="https://www.youtube.com/watch?v=KOCJJ27hhIE">Youtube<a/>.<sup/>
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</div>
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### Multiple Internet videos with a specific action (Coming soon)
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[](https://arxiv.org/pdf/2008.07931.pdf)
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<div align="center">
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<img src="doc/imocap/frame_00036_036.jpg" width="80%">
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</div>
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### Multiple views of multiple people (Comming soon)
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[](https://arxiv.org/pdf/1901.04111.pdf)
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### Others
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This project is used by many other projects:
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- [[CVPR21] Dense Reconstruction and View Synthesis from **Sparse Views**](https://zju3dv.github.io/neuralbody/)
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## Other features
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- [Camera calibration](./doc/todo.md)
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- [Pose guided synchronization](./doc/todo.md)
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- [Annotator](./doc/todo.md)
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- [Exporting of multiple data formats(bvh, asf/amc, ...)](./doc/todo.md)
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## Updates
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- 04/02/2021: We are now rebuilding our project for `v0.2`, please stay tuned. `v0.1` is available at [this link](https://github.com/zju3dv/EasyMocap/releases/tag/v0.1).
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## Installation
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### 1. Download SMPL models
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This step is the same as [smplx](https://github.com/vchoutas/smplx#model-loading).
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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.
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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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**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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├── J_regressor_body25_smplh.txt
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├── J_regressor_body25_smplx.txt
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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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├── smplh
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│ ├── MANO_LEFT.pkl
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│ ├── MANO_RIGHT.pkl
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│ ├── SMPLH_FEMALE.pkl
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│ └── SMPLH_MALE.pkl
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└── smplx
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├── SMPLX_FEMALE.pkl
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├── SMPLX_MALE.pkl
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└── SMPLX_NEUTRAL.pkl
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```
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### 2. Requirements
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- python>=3.6
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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](https://pyrender.readthedocs.io/en/latest/install/index.html#python-installation): for visualization
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- chumpy: for loading SMPL model
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- OpenPose[4]: for 2D pose
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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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See [doc/install](./doc/install.md) for more instructions.
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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)], which has 800 frames from 23 synchronized and calibrated cameras. 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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# 2.1 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 --gender male
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# 2.2 example for SMPL-X reconstruction
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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
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# 3.1 example for rendering SMPLX to ${out}/smpl
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python3 code/vis_render.py ${data} --out ${out} --skel ${out}/smpl --model smplx --gender male --undis --start 400 --sub_vis 1
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# 3.2 example for rendering skeleton of SMPL to ${out}/smplskel
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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
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```
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See [doc/quickstart](doc/quickstart.md) for more instructions.
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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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├── intri.yml
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├── extri.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> --handface
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```
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- `--openpose`: specify the openpose path
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- `--handface`: detect hands and face keypoints
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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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The input flags:
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- `--undis`: use to undistort the images
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- `--start, --end`: control the begin and end number of frames.
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The output flags:
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- `--vis_det`: visualize the detection
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- `--vis_repro`: visualize the reprojection
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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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### 3. Output
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Please refer to [output.md](doc/02_output.md)
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See [doc/notquickstart](doc/notquickstart.md) for more instructions.
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## Evaluation
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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.
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The weight parameters can be set according your data.
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We will add more quantitative reports in [doc/evaluation.md](doc/evaluation.md)
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More quantitative reports will be added in [doc/evaluation.md](doc/evaluation.md)
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## Acknowledgements
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Here are the great works this project is built upon:
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- SMPL models and layer are from MPII [SMPL-X model](https://github.com/vchoutas/smplx).
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@ -169,12 +93,14 @@ Here are the great works this project is built upon:
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We also would like to thank Wenduo Feng who is the performer in the sample data.
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## Contact
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Please open an issue if you have any questions.
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Please open an issue if you have any questions. We appreciate all contributions to improve our project.
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## Citation
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This project is a part of our work [iMocap](https://zju3dv.github.io/iMoCap/), [Mirrored-Human](https://zju3dv.github.io/Mirrored-Human/) and [Neural Body](https://zju3dv.github.io/neuralbody/)
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Please consider citing these works if you find this repo is useful for your projects.
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Please consider citing these works if you find this repo is useful for your projects.
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```bibtex
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@inproceedings{dong2020motion,
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BIN
doc/feng/mv1pmf-smplx.gif
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BIN
doc/feng/mv1pmf-smplx.gif
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53
doc/installation.md
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@ -0,0 +1,53 @@
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<!--
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* @Date: 2021-04-02 11:52:33
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* @Author: Qing Shuai
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* @LastEditors: Qing Shuai
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* @LastEditTime: 2021-04-02 11:52:59
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* @FilePath: /EasyMocapRelease/doc/installation.md
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-->
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# EasyMocap - Installation
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### 1. Download SMPL models
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This step is the same as [smplx](https://github.com/vchoutas/smplx#model-loading).
|
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|
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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.
|
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|
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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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**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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├── J_regressor_body25_smplh.txt
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├── J_regressor_body25_smplx.txt
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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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├── smplh
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│ ├── MANO_LEFT.pkl
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│ ├── MANO_RIGHT.pkl
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│ ├── SMPLH_FEMALE.pkl
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│ └── SMPLH_MALE.pkl
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└── smplx
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├── SMPLX_FEMALE.pkl
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├── SMPLX_MALE.pkl
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└── SMPLX_NEUTRAL.pkl
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```
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### 2. Requirements
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- python>=3.6
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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](https://pyrender.readthedocs.io/en/latest/install/index.html#python-installation): for visualization
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- chumpy: for loading SMPL model
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- OpenPose[4]: for 2D pose
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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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61
doc/notquickstart.md
Normal file
61
doc/notquickstart.md
Normal file
@ -0,0 +1,61 @@
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<!--
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||||
* @Date: 2021-04-02 11:53:55
|
||||
* @Author: Qing Shuai
|
||||
* @LastEditors: Qing Shuai
|
||||
* @LastEditTime: 2021-04-02 11:53:55
|
||||
* @FilePath: /EasyMocapRelease/doc/notquickstart.md
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-->
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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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├── intri.yml
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├── extri.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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|
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The input videos are placed in `videos/`.
|
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|
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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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|
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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> --handface
|
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```
|
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|
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- `--openpose`: specify the openpose path
|
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- `--handface`: detect hands and face keypoints
|
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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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|
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The input flags:
|
||||
|
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- `--undis`: use to undistort the images
|
||||
- `--start, --end`: control the begin and end number of frames.
|
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|
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The output flags:
|
||||
|
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- `--vis_det`: visualize the detection
|
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- `--vis_repro`: visualize the reprojection
|
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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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|
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### 3. Output
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|
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Please refer to [output.md](doc/02_output.md)
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26
doc/quickstart.md
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26
doc/quickstart.md
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@ -0,0 +1,26 @@
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||||
<!--
|
||||
* @Date: 2021-04-02 11:53:16
|
||||
* @Author: Qing Shuai
|
||||
* @LastEditors: Qing Shuai
|
||||
* @LastEditTime: 2021-04-02 11:53:16
|
||||
* @FilePath: /EasyMocapRelease/doc/quickstart.md
|
||||
-->
|
||||
|
||||
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
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||||
# 3.1 example for rendering SMPLX to ${out}/smpl
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||||
python3 code/vis_render.py ${data} --out ${out} --skel ${out}/smpl --model smplx --gender male --undis --start 400 --sub_vis 1
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||||
# 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
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||||
```
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||||
10
doc/todo.md
Normal file
10
doc/todo.md
Normal file
@ -0,0 +1,10 @@
|
||||
<!--
|
||||
* @Date: 2021-04-02 12:25:59
|
||||
* @Author: Qing Shuai
|
||||
* @LastEditors: Qing Shuai
|
||||
* @LastEditTime: 2021-04-02 12:26:08
|
||||
* @FilePath: /EasyMocapRelease/doc/todo.md
|
||||
-->
|
||||
# TODO
|
||||
|
||||
This part is coming soon, please stay tuned.
|
||||
@ -2,15 +2,20 @@
|
||||
* @Date: 2021-03-02 16:14:48
|
||||
* @Author: Qing Shuai
|
||||
* @LastEditors: Qing Shuai
|
||||
* @LastEditTime: 2021-03-02 17:09:02
|
||||
* @LastEditTime: 2021-03-27 21:56:34
|
||||
* @FilePath: /EasyMocap/scripts/calibration/Readme.md
|
||||
-->
|
||||
# Camera Calibration
|
||||
Before reading this document, you should read the OpenCV-Python Tutorials of [Camera Calibration](https://docs.opencv.org/master/dc/dbb/tutorial_py_calibration.html) carefully.
|
||||
|
||||
## Some Tips
|
||||
1. Use a chessboard as big as possible.
|
||||
2. You must keep the same resolution during all the steps.
|
||||
|
||||
## 0. Prepare your chessboard
|
||||
|
||||
## 1. Distortion and Intrinsic Parameter Calibration
|
||||
TODO
|
||||
|
||||
|
||||
## 2. Extrinsic Parameter Calibration
|
||||
Prepare your images as following:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user