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.
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.