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The 2D heatmap-based approaches have dominated Human Pose Estimation (HPE)for years due to high performance. However, the long-standing quantizationerror problem in the 2D heatmap-based methods leads to several well-knowndrawbacks: 1) The performance for the low-resolution inputs is limited; 2) Toimprove the feature map resolution for higher localization precision, multiplecostly upsampling layers are required; 3) Extra post-processing is adopted toreduce the quantization error. To address these issues, we aim to explore abrand new scheme, called \textit{SimCC}, which reformulates HPE as twoclassification tasks for horizontal and vertical coordinates. The proposedSimCC uniformly divides each pixel into several bins, thus achieving\emph{sub-pixel} localization precision and low quantization error. Benefitingfrom that, SimCC can omit additional refinement post-processing and excludeupsampling layers under certain settings, resulting in a more simple andeffective pipeline for HPE. Extensive experiments conducted over COCO,CrowdPose, and MPII datasets show that SimCC outperforms heatmap-basedcounterparts, especially in low-resolution settings by a large margin.

SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation