This software was developed by Manuel Kaufmann, Velko Vechev and Dario Mylonopoulos. The original aitviewer codebase is here. I just add a sloper4d_viwer.py file to visualize the SLOPER4D dataset.

Installation

Please install this fork locally

git clone https://github.com/climbingdaily/aitviewer.git
cd aitviewer
pip install -e .

Dependencies

Download the SMPL models to ./data/smpl_models/smpl/

For more advanced installation and for installing SMPL body models, please refer to the documentation .

Run

python sloper4d_viewer.py --pkl_file /path/to/the/pkl

optional parameters

--is_global  True    # whether to show the global translation of the person
--pose  'pose'       # the pose param to visualize, 'pose' or 'opt_pose'
--scene_path  /path/to/the/scenemesh

Projects using the aitviewer

A sampling of projects using the aitviewer. Let us know if you want to be added to this list!

• Sun et al., TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments, CVPR 2023
• Fan et al., ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation, CVPR 2023
• Dong et al., Shape-aware Multi-Person Pose Estimation from Multi-view Images, ICCV 2021
• Kaufmann et al., EM-POSE: 3D Human Pose Estimation from Sparse Electromagnetic Trackers, ICCV 2021
• Vechev et al., Computational Design of Kinesthetic Garments, Eurographics 2021
• Guo et al., Human Performance Capture from Monocular Video in the Wild, 3DV 2021
• Dong and Guo et al., PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence, CVPR 2022

Citation

If you use this software, please cite it as below.

@software{Kaufmann_Vechev_aitviewer_2022,
  author = {Kaufmann, Manuel and Vechev, Velko and Mylonopoulos, Dario},
  doi = {10.5281/zenodo.1234},
  month = {7},
  title = {{aitviewer}},
  url = {https://github.com/eth-ait/aitviewer},
  year = {2022}
}