{"id":666252,"date":"2020-06-12T14:12:33","date_gmt":"2020-06-12T21:12:33","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=666252"},"modified":"2020-06-14T09:15:58","modified_gmt":"2020-06-14T16:15:58","slug":"higherhrnet-scale-aware-representation-learning-for-bottom-up-human-pose-estimation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/higherhrnet-scale-aware-representation-learning-for-bottom-up-human-pose-estimation\/","title":{"rendered":"HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation"},"content":{"rendered":"

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<\/strong>: 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 multiresolution 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 topdown 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 (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

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. 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