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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
Last synced: 4 months ago
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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
- Host: GitHub
- URL: https://github.com/facebookresearch/DensePose
- Owner: facebookresearch
- License: other
- Archived: true
- Created: 2018-06-04T18:52:54.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2023-01-18T17:26:59.000Z (about 2 years ago)
- Last Synced: 2024-09-26T21:43:45.619Z (5 months ago)
- Language: Jupyter Notebook
- Homepage: http://densepose.org
- Size: 11.7 MB
- Stars: 6,952
- Watchers: 250
- Forks: 1,296
- Open Issues: 151
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
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.
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}
}
```