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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.

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:

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}
  }