HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

• Published in: Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 5385 - 5394
• Main Authors: Cheng, Bowen, Xiao, Bin, Wang, Jingdong, Shi, Honghui, Huang, Thomas S., Zhang, Lei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2020
• Subjects: Convolution; Deconvolution; Heating systems; Pose estimation; Spatial resolution; Training
• ISSN: 1063-6919
• DOI: 10.1109/CVPR42600.2020.00543

Bottom-up human pose estimation methods have difficulties in predicting the correct pose for small persons due to challenges in scale variation. In this paper, we present HigherHRNet: a novel bottom-up human pose estimation method for learning scale-aware representations using high-resolution feature pyramids. Equipped with multi-resolution supervision for training and multi-resolution aggregation for inference, the proposed approach is able to solve the scale variation challenge in bottom-up multi-person pose estimation and localize keypoints more precisely, especially for small person. The feature pyramid in HigherHRNet consists of feature map outputs from HRNet and upsampled higher-resolution outputs through a transposed convolution. HigherHRNet outperforms the previous best bottom-up method by 2.5% AP for medium person on COCO test-dev, showing its effectiveness in handling scale variation. Furthermore, HigherHRNet achieves new state-of-the-art result on COCO test-dev (70.5% AP) without using refinement or other post-processing techniques, surpassing all existing bottom-up methods. HigherHRNet even surpasses all top-down methods on CrowdPose test (67.6% AP), suggesting its robustness in crowded scene.