A set of tools to visualize and interact with sequences of 3D data with cross-platform support on Windows, Linux, and macOS. See the official page at https://eth-ait.github.io/aitviewer for all the details.

Installation

Basic Installation:

pip install aitviewer

Note that this does not install the GPU-version of PyTorch automatically. If your environment already contains it, you should be good to go, otherwise install it manually.

Or install locally (if you need to extend or modify code)

git clone git@github.com:eth-ait/aitviewer.git
cd aitviewer
pip install -e .

On macOS with Apple Silicon it is recommended to use PyQt6. Please check this issue for installation instructions.

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

Features

• Native Python interface, easy to use and hack.
• Load SMPL[-H/-X] / MANO / FLAME / STAR / SUPR sequences and display them in an interactive viewer.
• Headless mode for server rendering of videos/images.
• Remote mode for non-blocking integration of visualization code.
• Render 3D data on top of images via weak-perspective or OpenCV camera models.
• Animatable camera paths.
• Edit SMPL sequences and poses manually.
• Prebuilt renderable primitives (cylinders, spheres, point clouds, etc).
• Built-in extensible GUI (based on Dear ImGui).
• Export screenshots, videos and turntable views (as mp4/gif)
• High-Performance ModernGL-based rendering pipeline (running at 100fps+ on most laptops).

Quickstart

Display an SMPL T-pose (Requires SMPL models):

from aitviewer.renderables.smpl import SMPLSequence
from aitviewer.viewer import Viewer

if __name__ == '__main__':
    v = Viewer()
    v.scene.add(SMPLSequence.t_pose())
    v.run()

Projects using the aitviewer

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

• Fan et al., HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video, CVPR 2024
• Braun et al., Physically Plausible Full-Body Hand-Object Interaction Synthesis, 3DV 2024
• Zhang and Christen et al., ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation, 3DV 2024
• Kaufmann et al., EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild, ICCV 2023
• Shen and Guo et al., X-Avatar: Expressive Human Avatars, CVPR 2023
• 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 and Guo et al., PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequence, CVPR 2022
• 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

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.10013305},
  month = {7},
  title = {{aitviewer}},
  url = {https://github.com/eth-ait/aitviewer},
  year = {2022}
}

Contact & Contributions

This software was developed by Manuel Kaufmann, Velko Vechev and Dario Mylonopoulos. For questions please create an issue. We welcome and encourage module and feature contributions from the community.