June 18, 2018
DensePose: Dense Human Pose Estimation In The Wild
Computer Vision and Pattern Recognition (CVPR)
In this work we establish dense correspondences between an RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation. We gather dense correspondences for 50K persons appearing in the COCO dataset by introducing an efficient annotation pipeline. We then use our dataset to train CNN-based systems that deliver dense correspondence ‘in the wild’, namely in the presence of background, occlusions and scale variations.
By: Riza Alp Guler, Natalia Neverova, Iasonas Kokkinos