Overview

The official page for aitviewer, a set of tools to visualize and interact with sequences of 3D data with cross-platform support on Windows, Linux, and macOS.

Features

Native Python interface, easy to use and hack.
Load SMPL[-H/-X] / MANO / FLAME / STAR 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).

AITV SMPL Editing

Installation

Basic Installation:

pip install aitviewer

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 .

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.

If you would like to visualize STAR, please install the package manually via

pip install git+https://github.com/ahmedosman/STAR.git

and download the respective body models from the official website.

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 add you!

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 = ,
  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.

AITV Sample