DensePose in Detectron2

DensePose aims at learning and establishing dense correspondences between image pixels and 3D object geometry for deformable objects, such as humans or animals. In this repository, we provide the code to train and evaluate DensePose R-CNN and various tools to visualize DensePose annotations and results.

There are two main paradigms that are used within DensePose project.

Chart-based Dense Pose Estimation for Humans and Animals

For chart-based estimation, 3D object mesh is split into charts and for each pixel the model estimates chart index I and local chart coordinates (U, V). Please follow the link above to find a detailed overview of the method, links to trained models along with their performance evaluation in the Model Zoo and references to the corresponding papers.

Continuous Surface Embeddings for Dense Pose Estimation for Humans and Animals

To establish continuous surface embeddings, the model simultaneously learns descriptors for mesh vertices and for image pixels. The embeddings are put into correspondence, thus the location of each pixel on the 3D model is derived. Please follow the link above to find a detailed overview of the method, links to trained models along with their performance evaluation in the Model Zoo and references to the corresponding papers.

Quick Start

See Getting Started

Model Zoo

Please check the dedicated pages for chart-based model zoo and for continuous surface embeddings model zoo.

What's New

License

Detectron2 is released under the Apache 2.0 license

Citing DensePose

If you use DensePose, please refer to the BibTeX entries for chart-based models and for continuous surface embeddings.