Figure 1: Dense pose estimation aims at mapping all human pixels of an RGB image to the 3D surface of the human body. We introduce DensePose-COCO, a large-scale ground-truth dataset containing manually annotated image-to-surface correspondences for 50K images, and train DensePose-RCNN to densely regress UV coordinates at multiple frames per second.
For human pose estimation, Güler et al. REF segment the 3D surface of the human body into semantically-defined parts.
DensePose: Dense Human Pose Estimation in the Wild
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science