https://github.com/isarandi/affine-combining-autoencoder
Compress point lists (e.g., 3D human skeleton definitions) through affine combinations (WACV2023)
https://github.com/isarandi/affine-combining-autoencoder
Last synced: 4 months ago
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Compress point lists (e.g., 3D human skeleton definitions) through affine combinations (WACV2023)
- Host: GitHub
- URL: https://github.com/isarandi/affine-combining-autoencoder
- Owner: isarandi
- License: agpl-3.0
- Created: 2023-02-16T23:24:32.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-12T04:23:51.000Z (5 months ago)
- Last Synced: 2024-12-12T05:17:26.269Z (5 months ago)
- Language: Python
- Size: 23.4 KB
- Stars: 7
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Affine-Combining Autoencoder (ACAE)
This repository contains code for the paper "Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats" by István Sárándi, Alexander Hermans, and Bastian Leibe.
The paper relies also on code from several other repos, including [metrabs](https://github.com/isarandi/metrabs) and [posepile](https://github.com/isarandi/posepile). This repo is specifically for the ACAE part.
See the `train_acae` function in `acae.py` for how to train an ACAE.
## Publication reference
If you find this code useful, consider citing the paper:
```bibtex
@inproceedings{Sarandi2023acae,
author = {S\'ar\'andi, Istv\'an and Hermans, Alexander and Leibe, Bastian},
title = {Learning {3D} Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year = {2023}
}
```
## License
GNU Affero General Public License, Version 3 (AGPL-3.0)
## Legal disclaimer
This software is a research prototype only and shall only be used for test-purposes. This software must not be used in or for products and/or services and in particular not in or for safety-relevant areas. It was solely developed for and
published as part of the publication ‘Learning 3D Human Pose Estimation From
Dozens of Datasets Using a Geometry-Aware Autoencoder To Bridge Between
Skeleton Formats’ and will neither be maintained nor monitored in any way.