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# DensePose in Detectron2
**Dense Human Pose Estimation In The Wild**
_Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos_
[[`densepose.org`](https://densepose.org)] [[`arXiv`](https://arxiv.org/abs/1802.00434)] [[`BibTeX`](#CitingDensePose)]
Dense human pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body.
<div align="center">
<img src="https://drive.google.com/uc?export=view&id=1qfSOkpueo1kVZbXOuQJJhyagKjMgepsz" width="700px" />
</div>
In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide tools to visualize
DensePose annotation and results.
# Quick Start
See [ Getting Started ](doc/GETTING_STARTED.md)
# Model Zoo and Baselines
We provide a number of baseline results and trained models available for download. See [Model Zoo](doc/MODEL_ZOO.md) for details.
# License
Detectron2 is released under the [Apache 2.0 license](../../LICENSE)
## <a name="CitingDensePose"></a>Citing DensePose
If you use DensePose, please take the references from the following BibTeX entries:
For DensePose with estimated confidences:
```
@InProceedings{Neverova2019DensePoseConfidences,
title = {Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels},
author = {Neverova, Natalia and Novotny, David and Vedaldi, Andrea},
journal = {Advances in Neural Information Processing Systems},
year = {2019},
}
```
For the original DensePose:
```
@InProceedings{Guler2018DensePose,
title={DensePose: Dense Human Pose Estimation In The Wild},
author={R\{i}za Alp G\"uler, Natalia Neverova, Iasonas Kokkinos},
journal={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2018}
}
```
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