  This paper investigates the task of 2D whole-body human pose estimation,
which aims to localize dense landmarks on the entire human body including body,
feet, face, and hands. We propose a single-network approach, termed ZoomNet, to
take into account the hierarchical structure of the full human body and solve
the scale variation of different body parts. We further propose a neural
architecture search framework, termed ZoomNAS, to promote both the accuracy and
efficiency of whole-body pose estimation. ZoomNAS jointly searches the model
architecture and the connections between different sub-modules, and
automatically allocates computational complexity for searched sub-modules. To
train and evaluate ZoomNAS, we introduce the first large-scale 2D human
whole-body dataset, namely COCO-WholeBody V1.0, which annotates 133 keypoints
for in-the-wild images. Extensive experiments demonstrate the effectiveness of
ZoomNAS and the significance of COCO-WholeBody V1.0.
