  This paper investigates the task of 2D human whole-body pose estimation,
which aims to localize dense landmarks on the entire human body including face,
hands, body, and feet. As existing datasets do not have whole-body annotations,
previous methods have to assemble different deep models trained independently
on different datasets of the human face, hand, and body, struggling with
dataset biases and large model complexity. To fill in this blank, we introduce
COCO-WholeBody which extends COCO dataset with whole-body annotations. To our
best knowledge, it is the first benchmark that has manual annotations on the
entire human body, including 133 dense landmarks with 68 on the face, 42 on
hands and 23 on the body and feet. A single-network model, named ZoomNet, is
devised to take into account the hierarchical structure of the full human body
to solve the scale variation of different body parts of the same person.
ZoomNet is able to significantly outperform existing methods on the proposed
COCO-WholeBody dataset. Extensive experiments show that COCO-WholeBody not only
can be used to train deep models from scratch for whole-body pose estimation
but also can serve as a powerful pre-training dataset for many different tasks
such as facial landmark detection and hand keypoint estimation. The dataset is
publicly available at https://github.com/jin-s13/COCO-WholeBody.
