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https://github.com/facebookresearch/DensePose

A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body
https://github.com/facebookresearch/DensePose

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A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body

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README 

        
# DensePose: 

**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. 

DensePose-RCNN is implemented in the [Detectron](https://github.com/facebookresearch/Detectron) framework and is powered by [Caffe2](https://github.com/caffe2/caffe2).





  




In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide notebooks to visualize the collected DensePose-COCO dataset and show the correspondences to the SMPL model.



## Important Note



**!!! This project is no longer supported !!!**



DensePose is now part of Detectron2 (https://github.com/facebookresearch/detectron2/tree/master/projects/DensePose). There you can find the most up to date architectures / models. If you think some feature is missing from there, please post an issue in [Detectron2 DensePose](https://github.com/facebookresearch/detectron2/tree/master/projects/DensePose).



## Installation



Please find installation instructions for Caffe2 and DensePose in [`INSTALL.md`](INSTALL.md), a document based on the [Detectron](https://github.com/facebookresearch/Detectron) installation instructions.



## Inference-Training-Testing



After installation, please see [`GETTING_STARTED.md`](GETTING_STARTED.md)  for examples of inference and training and testing.



## Notebooks



### Visualization of DensePose-COCO annotations:



See [`notebooks/DensePose-COCO-Visualize.ipynb`](notebooks/DensePose-COCO-Visualize.ipynb) to visualize the DensePose-COCO annotations on the images:





  




---



### DensePose-COCO in 3D:



See [`notebooks/DensePose-COCO-on-SMPL.ipynb`](notebooks/DensePose-COCO-on-SMPL.ipynb) to localize the DensePose-COCO annotations on the 3D template ([`SMPL`](http://smpl.is.tue.mpg.de)) model:





  




---

### Visualize DensePose-RCNN Results:



See [`notebooks/DensePose-RCNN-Visualize-Results.ipynb`](notebooks/DensePose-RCNN-Visualize-Results.ipynb) to visualize the inferred DensePose-RCNN Results.





  




---

### DensePose-RCNN Texture Transfer:



See [`notebooks/DensePose-RCNN-Texture-Transfer.ipynb`](notebooks/DensePose-RCNN-Texture-Transfer.ipynb) to localize the DensePose-COCO annotations on the 3D template ([`SMPL`](http://smpl.is.tue.mpg.de)) model:





  




## License



This source code is licensed under the license found in the [`LICENSE`](LICENSE) file in the root directory of this source tree.



## Citing DensePose



If you use Densepose, please use the following BibTeX entry.



```

  @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}

  }

```