DensePose


DensePose, dense human pose estimation, is designed to map all human pixels of an RGB image to a 3D surface-based representation of the human body. This is done through the introduction of a large-scale, manually annotated dataset, and a variant of Mask-RCNN, a simple, flexible framework for object instance segmentation.

Mapping images to 3D surfaces

DensePose establishes dense correspondences between RGB images and a surface-based representation of the human body. To do this, AI researchers built DensePose-COCO, a large-scale, ground-truth dataset with image-to-surface correspondences annotated on 50,000 COCO images.

The DensePose-COCO dataset was used to train DensePose-RCNN, a CNN-based system that delivers dense correspondences “in the wild”, namely in the presence of complex backgrounds, occlusions, and scale variations. Portions of the DensePose research project will be open sourced soon.


Get Started

Coming soon

More Tools

Detectron

Detectron is a high-performance codebase for object detection, covering bounding box and object instance segmentation outputs.

Join Us

Tackle the world's most complex technology challenges

Join Our Team

Latest News

Visit the AI Blog for updates on recent publications, new tools, and more.

Visit Blog