From 55b3fef9896d158e4a96a2bd57ba16122eab5853 Mon Sep 17 00:00:00 2001
From: shuaiqing <s_q@zju.edu.cn>
Date: Thu, 14 Jan 2021 21:17:40 +0800
Subject: [PATCH] init

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+<!--
+ * @Date: 2021-01-13 20:32:12
+ * @Author: Qing Shuai
+ * @LastEditors: Qing Shuai
+ * @LastEditTime: 2021-01-14 20:46:21
+ * @FilePath: /EasyMocapRelease/Readmd.md
+-->
+# EasyMocap
+**EasyMocap** is an open-source toolbox for **markerless human motion capture**.
+
+## Results
+|:heavy_check_mark: Skeleton|:heavy_check_mark: SMPL|
+|----|----|
+|![repro](doc/feng/repro_512.gif)|![repro](doc/feng/smpl_512.gif)||
+
+|:black_square_button: Whole Body|:black_square_button: [Detailed Mesh](https://zju3dv.github.io/neuralbody/)|
+|----|----|
+|<div align="center"><img src="doc/feng/total_512.gif" height="200" alt="3DPW" align=center /></div>|<div align="center"><img src="doc/feng/body_256.gif" height="200" width="200" alt="3DPW" align=center />
+</div>|
+
+## 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
+```
+<!-- 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.  -->
+
+## Quick Start
+We provide an example multiview dataset[OneDrive](). 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
+python3 scripts/preprocess/extract_video.py ${data} --openpose <openpose_path> 
+```
+
+### 2. Run the code
+
+### 3. Output
+The results are saved in `json` format.
+```bash
+- <output_root>
+├── 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': <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": <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 some great resources we benefit:
+
+- SMPL models and layer is borrowed 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)
+
+We also would like to thank Wenduo Feng for the example data.
+
+## Contact
+Please open an issue if you have any questions.
+
+## Citation
+This project is the base of our other works: [iMocap](https://zju3dv.github.io/iMoCap/), [Neural Body](https://zju3dv.github.io/neuralbody/)
+
+```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}
+}
+```
+
+<!-- ## License -->