# densepose

**Repository Path**: Yang_Feng1/densepose

## Basic Information

- **Project Name**: densepose
- **Description**: Dense Human Pose Estimation In The Wild
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-05-06
- **Last Updated**: 2024-10-14

## Categories & Tags

**Categories**: ai

**Tags**: None

## 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).

<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 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:

<div align="center">
  <img src="https://drive.google.com/uc?export=view&id=1uYRJkIA24KkJU2i4sMwrKa61P0xtZzHk" width="800px" />
</div>

---

### 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:

<div align="center">
  <img src="https://drive.google.com/uc?export=view&id=1m32oyMuE7AZd3EOf9k8zHpr75C8bHlYj" width="500px" />
</div>

---
### Visualize DensePose-RCNN Results:

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

<div align="center">
  <img src="https://drive.google.com/uc?export=view&id=1k4HtoXpbDV9MhuyhaVcxDrXnyP_NX896" width="900px" />
</div>

---
### 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:

<div align="center">
  <img src="https://drive.google.com/uc?export=view&id=1r-w1oDkDHYnc1vYMbpXcYBVD1-V3B4Le" width="900px" />
</div>

## License

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

## <a name="CitingDensePose"></a>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}
  }
```