cloudrender: an OpenGL framework for pointcloud and mesh rendering

A visualization framework capable of rendering large pointclouds, dynamic SMPL models and more. Used to visualize results in our Human POSEitioning System (HPS) project: http://virtualhumans.mpi-inf.mpg.de/hps/

Requirements

• GPU with OpenGL 4.0

Optionally, if you want to run included test script:

• EGL support (for headless rendering)
• ffmpeg>=2.1 with libx264 enabled and ffprobe installed (for saving to video)

Installation

Step 1. Get the code

Copy the code without installation

git clone https://github.com/vguzov/cloudrender
pip install -r requirements.txt

or install as a package with

pip install cloudrender

Step 2. Get the SMPL model

• Follow install instructions at https://github.com/gulvarol/smplpytorch
• Make sure to fix the typo for male model while unpacking SMPL .pkl files: basicmodel_m_lbs_10_207_0_v1.0.0.pkl -> basicModel_m_lbs_10_207_0_v1.0.0.pkl

Running test script

test_scene_video.py

Run download_test_assets.sh – it will create test_assets folder and download everything you need for sample to work (3D scan pointcloud, human shape and motion files, camera trajectory file)

Run test_scene_video.py

The following script will write a short video inside test_assets/output.mp4 which should look similar to this:

More data

Please check our HPS project page for more 3D scans and motion data: http://virtualhumans.mpi-inf.mpg.de/hps/

Citation

If you find the code or data useful, please cite:

@inproceedings{HPS,
    title = {Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors },
    author = {Guzov, Vladimir and Mir, Aymen and Sattler, Torsten and Pons-Moll, Gerard},
    booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {jun},
    organization = {{IEEE}},
    year = {2021},
}