DensePose in Detectron2

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.

In this repository, we provide the code to train and evaluate DensePose-RCNN. We also provide tools to visualize DensePose annotation and results.

Quick Start

See Getting Started

Model Zoo and Baselines

We provide a number of baseline results and trained models available for download. See Model Zoo for details.

License

Detectron2 is released under the Apache 2.0 license

Citing DensePose

If you use DensePose, please take the references from the following BibTeX entries:

For DensePose with estimated confidences:

@InProceedings{Neverova2019DensePoseConfidences,
    title = {Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels},
    author = {Neverova, Natalia and Novotny, David and Vedaldi, Andrea},
    journal = {Advances in Neural Information Processing Systems},
    year = {2019},
}

For the original DensePose:

@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}
}