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

Understand

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.