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.
Installation
Please find installation instructions for Caffe2 and DensePose in INSTALL.md, a document based on the Detectron installation instructions.
Inference-Training-Testing
After installation, please see GETTING_STARTED.md for examples of inference and training and testing.
This source code is licensed under the license found in the 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}
}