Dense Human Pose Estimation In The Wild
Rıza Alp Güler, Natalia Neverova, Iasonas Kokkinos
[densepose.org
] [arXiv
] [BibTeX
]
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 framework and is powered by 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.
Please find installation instructions for Caffe2 and DensePose in INSTALL.md
, a docment based on the Detectron installation instructions.
After installation, please see GETTING_STARTED.md
for examples of inference and training and testing.
See notebooks/DensePose-COCO-Visualize.ipynb
to visualize the DensePose-COCO annotations on the images:
See notebooks/DensePose-COCO-on-SMPL.ipynb
to localize the DensePose-COCO annotations on the 3D template (SMPL
) model:
See notebooks/DensePose-RCNN-Visualize-Results.ipynb
to visualize the inferred DensePose-RCNN Results.
See notebooks/DensePose-RCNN-Texture-Transfer.ipynb
to localize the DensePose-COCO annotations on the 3D template (SMPL
) model:
This source code is licensed under the license found in the 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}
}