garyfeng/densepose repository overview

⁠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.md⁠ for examples of inference and training and testing.

⁠Notebooks

⁠Visualization of DensePose-COCO annotations:

See 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 to localize the DensePose-COCO annotations on the 3D template (SMPL⁠) model:

⁠Visualize DensePose-RCNN Results:

See notebooks/DensePose-RCNN-Visualize-Results.ipynb⁠ to visualize the inferred DensePose-RCNN Results.

⁠DensePose-RCNN Texture Transfer:

See notebooks/DensePose-RCNN-Texture-Transfer.ipynb⁠ to localize the DensePose-COCO annotations on the 3D template (SMPL⁠) model:

⁠License

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}
  }