Skip to content

Latest commit

 

History

History
77 lines (58 loc) · 2.25 KB

mvmp.md

File metadata and controls

77 lines (58 loc) · 2.25 KB
Raw

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 for more details.

0. Preparation

Prepare your calibrated and synchronized system by yourself.

You can download our dataset here.

├── 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:

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.

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.

python3 apps/demo/auto_track.py ${data}/output ${data}/output-track --track3d

Then we can fit SMPL model to the tracked keyponts:

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

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.

mvmpmf.mp4