The official repository of the RePoGen paper.
RePoGen (RarE POses GENerator) is a method for synthetic data generation using the SMPL-X. RePoGen generates humans in very rare poses using the estimation of rotation distribution for each SMPL-X joint. The generated poses are used to augment the COCO dataset to improve performance on extreme views.
- 1 June 2024: Winner of the Best Poster Award on FG 2024
- 5 March 2024: The paper got accepted for 18th IEEE International Conferene on Automatic Face and gesture Recognition (FG 2024)
- 17 July 2023: Code released
- 17 July 2023: Datasets and weights released
- 16 July 2023: The Readme along with description is available. You can read the paper and see its website. The code is comming soon.
The RePoGen is installed from the source:
git clone https://github.com/MiraPurkrabek/RePoGen
python setup.py installThe code requires psbody.mesh library from MPI-IS (link). For easiest installation, please use our fork of the repository and install it from source. For more details, see issue #12.
You also have to download the SMPL-X model. See the instructions here.
Once you install everything right, data are generated by the script here.
Introduced datasets are available to download on the project webpage. The RePo dataset was manually annotated while the RePoGen dataset was generated by the RePoGen method. We also give you the code in this repository to generate your own synthetic dataset.
If you are only interested in the pre-trained weights, we release the best model trained on the COCO+RePoGen dataset here. The weights are for the ViTPose-s with classic decoder.
Please read carefully the terms and conditions and any accompanying documentation before you download and/or use the RePoGen model, data and software, (the "Model & Software"). By downloading and/or using the Model & Software (including downloading, cloning, installing, and any other use of this github repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Model & Software. Any infringement of the terms of this agreement will automatically terminate your rights under this License.
Please also note that the RePoGen depends on the SMPL-X which is free to use only for non-commercial scientific research purposes. For more, see their homepage.
The code builds on the SMPL-X repository.
For experiments, we used the COCO dataset and the PoseFES dataset.
The code was implemented by Miroslav Purkrábek.
For questions, please use the Issues of Discussion.
@INPROCEEDINGS{purkrabek2024improving,
author={Purkrabek, Miroslav and Matas, Jiri},
booktitle={2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)},
title={Improving 2D Human Pose Estimation in Rare Camera Views with Synthetic Data},
year={2024},
volume={},
number={},
pages={1-9},
keywords={Space vehicles;Training;Three-dimensional displays;Pose estimation;Gesture recognition;Data models;Orbits},
doi={10.1109/FG59268.2024.10582011}}
