# EasyMocap - mvmp This code aims to solve the problem of reconstructing multiple persons from multiple calibrated cameras. The released code is an easy-to-use version. See [Advanced](#Advanced) for more details. ## 0. Preparation Prepare your calibrated and synchronized system by yourself. You can download our dataset [here](https://zjueducn-my.sharepoint.com/:u:/g/personal/s_q_zju_edu_cn/EZFGgpK2Y6RBkPbGvny_PC0BIS08qJvxGYEHYopjhHX_TQ?e=LY3pgm). ```bash ├── intri.yml ├── extri.yml ├── annots │   ├── 0 │   ├── 1 │   ├── 2 │   ├── 3 │   ├── 4 │   ├── 5 │   ├── 6 │   └── 7 └── videos ├── 0.mp4 ├── 1.mp4 ├── 2.mp4 ├── 3.mp4 ├── 4.mp4 ├── 5.mp4 ├── 6.mp4 └── 7.mp4 ``` Extract the images from videos: ```bash data=/path/to/data python3 scripts/preprocess/extract_video.py ${data} --no2d ``` ## 1. Reconstucting human pose This step will reconstruct the human pose in each frame. ```bash python3 apps/demo/mvmp.py ${data} --out ${data}/output --annot annots --cfg config/exp/mvmp1f.yml --undis --vis_det --vis_repro ``` ## 2. Recovering SMPL body model First we should tract the human pose in each frame. This step will track and interpolate missing frames. ```bash python3 apps/demo/auto_track.py ${data}/output ${data}/output-track --track3d ``` Then we can fit SMPL model to the tracked keyponts: ```bash python3 apps/demo/smpl_from_keypoints.py ${data} --skel ${data}/output-track/keypoints3d --out ${data}/output-track/smpl --verbose --opts smooth_poses 1e1 ``` To visualize the results, see [visualization tutorial](./doc/realtime_visualization.md) ## Advanced For more complicated scenes, our lab has a real-time version of this algorithm, which can perform 3D reconstruction and tracking simultaneously. If you want to use this part for commercial queries, please contact [Xiaowei Zhou](mailto:xwzhou@zju.edu.cn). https://user-images.githubusercontent.com/22812405/123629197-968c0080-d846-11eb-8417-4e6d3a65466d.mp4