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.
@article{arxiv.2208.11547,
title = {ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild},
author = {Lumin Xu and Sheng Jin and Wentao Liu and Chen Qian and Wanli Ouyang and Ping Luo and Xiaogang Wang},
journal= {arXiv preprint arXiv:2208.11547},
year = {2022}
}