A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of th...
Available items
No Items, yet!
The developer of this repository has not created any items for sale yet. Need a bug fixed? Help with integration? A different license? Create a request here:
DensePose:
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.
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.mdfor examples of inference and training and testing.
Notebooks
Visualization of DensePose-COCO annotations:
notebooks/DensePose-COCO-Visualize.ipynbto visualize the DensePose-COCO annotations on the images:
DensePose-COCO in 3D:
notebooks/DensePose-COCO-on-SMPL.ipynbto localize the DensePose-COCO annotations on the 3D template (
SMPL) model:
Visualize DensePose-RCNN Results:
notebooks/DensePose-RCNN-Visualize-Results.ipynbto visualize the inferred DensePose-RCNN Results.
DensePose-RCNN Texture Transfer:
notebooks/DensePose-RCNN-Texture-Transfer.ipynbto localize the DensePose-COCO annotations on the 3D template (
SMPL) model:
License
This source code is licensed under the license found in the
LICENSEfile 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}
}