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