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Current methods of multi-person pose estimation typically treat thelocalization and the association of body joints separately. It is convenientbut inefficient, leading to additional computation and a waste of time. Thispaper, however, presents a novel framework PoseDet (Estimating Pose byDetection) to localize and associate body joints simultaneously at higherinference speed. Moreover, we propose the keypoint-aware pose embedding torepresent an object in terms of the locations of its keypoints. The proposedpose embedding contains semantic and geometric information, allowing us toaccess discriminative and informative features efficiently. It is utilized forcandidate classification and body joint localization in PoseDet, leading torobust predictions of various poses. This simple framework achieves anunprecedented speed and a competitive accuracy on the COCO benchmark comparedwith state-of-the-art methods. Extensive experiments on the CrowdPose benchmarkshow the robustness in the crowd scenes. Source code is available.

PoseDet: Fast Multi-Person Pose Estimation Using Pose Embedding