  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. The code and models are available at
https://github.com/HRNet/Higher-HRNet-Human-Pose-Estimation.
