| # 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} | |
| } | |
| ``` | |