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Human pose estimation are of importance for visual understanding tasks suchas action recognition and human-computer interaction. In this work, we presenta Multiple Stage High-Resolution Network (Multi-Stage HRNet) to tackling theproblem of multi-person pose estimation in images. Specifically, we follow thetop-down pipelines and high-resolution representations are maintained duringsingle-person pose estimation. In addition, multiple stage network and crossstage feature aggregation are adopted to further refine the keypoint position.The resulting approach achieves promising results in COCO datasets. Oursingle-model-single-scale test configuration obtains 77.1 AP score in test-devusing publicly available training data.

Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation