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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social distancing, and body orientation.
| Date | Stars |
|---|---|
| 2026-07-24 | 460 |
| 2026-07-25 | 460 |
| 2026-07-28 | 460 |
| 2026-07-30 | 460 |
| 2026-08-06 | 460 |
Today
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growth rate 0.00%/day
# Monoloco library [](https://pepy.tech/project/monoloco)
Continuously tested on Linux, MacOS and Windows: [](https://github.com/vita-epfl/monoloco/actions?query=workflow%3ATests)
<img src="docs/webcam.gif" width="700" alt="gif" />
<br />
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This library is based on three research projects for monocular/stereo 3D human localization (detection), body orientation, and social distancing. Check the __video teaser__ of the library on [__YouTube__](https://www.youtube.com/watch?v=O5zhzi8mwJ4).
---
> __MonStereo: When Monocular and Stereo Meet at the Tail of 3D Human Localization__<br />
> _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com),
[T. Mordan](https://people.epfl.ch/taylor.mordan/?lang=en), [A. Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, ICRA 2021 <br />
__[Article](https://arxiv.org/abs/2008.10913)__ __[Citation](#Citation)__ __[Video](https://www.youtube.com/watch?v=pGssROjckHU)__
<img src="docs/out_000840_multi.jpg" width="700"/>
---
> __Perceiving Humans: from Monocular 3D Localization to Social Distancing__<br />
> _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com),
[A. Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, T-ITS 2021 <br />
__[Article](https://arxiv.org/abs/2009.00984)__ __[Citation](#Citation)__ __[Video](https://www.youtube.com/watch?v=r32UxHFAJ2M)__
<img src="docs/social_distancing.jpg" width="700"/>
---
> __MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation__<br />
> _[L. Bertoni](https://scholar.google.com/citations?user=f-4YHeMAAAAJ&hl=en), [S. Kreiss](https://www.svenkreiss.com), [A.Alahi](https://scholar.google.com/citations?user=UIhXQ64AAAAJ&hl=en)_, ICCV 2019 <br />
__[Article](https://arxiv.org/abs/1906.06059)__ __[Citation](#Citation)__ __[Video](https://www.youtube.com/watch?v=ii0fqerQrec)__
<img src="docs/surf.jpg" width="700"/>
## Library Overview
Visual illustration of the library components:
<img src="docs/monoloco.gif" width="700" alt="gif" />
## License
All projects are built upon [Openpifpaf](https://github.com/vita-epfl/openpifpaf) for the 2D keypoints and share the AGPL Licence.
This software is also available for commercial licensing via the EPFL Technology Transfer
Office (https://tto.epfl.ch/, [email protected]).
## Quick setup
A GPU is not required, yet highly recommended for real-time performances.
The installation has been tested on OSX and Linux operating systems, with Python 3.6, 3.7, 3.8.
Packages have been installed with pip and virtual environments.
For quick installation, do not clone this repository, make sure there is no folder named monoloco in your current directory, and run:
```
pip3 install monoloco
```
For development of the source code itself, you need to clone this repository and then:
```
pip3 install sdist
cd monoloco
python3 setup.py sdist bdist_wheel
pip3 install -e .
```
### Interfaces
All the commands are run through a main file called `run.py` using subparsers.
To check all the options:
* `python3 -m monoloco.run --help`
* `python3 -m monoloco.run predict --help`
* `python3 -m monoloco.run train --help`
* `python3 -m monoloco.run eval --help`
* `python3 -m monoloco.run prep --help`
or check the file `monoloco/run.py`
# Predictions
The software receives an image (or an entire folder using glob expressions),
calls PifPaf for 2D human posExcerpt of 18,745 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2d4f4653db5edbae, topic:computer-vision, topic:object-detection, topic:pose-estimation
matched fp:2d4f4653db5edbae, topic:deep-learning, topic:pytorch