Revising Representation and Target Deviations for Accurate Human Pose Estimation

Owing to the normalized instance scales and robust supervision, heatmap-based human pose estimation (HPE) methods with top-down paradigm have achieved a dominant performance. However, there are two inherent deviations in the basic framework, i.e., representation and target deviations, resulting in performance bottlenecks. The representation deviation is caused by transforming various scales of instances into a unified input size, which results in performance degradation because data with different scale-related characteristics can hardly be handled via unified parameters. The target deviation is caused by exploiting a prior distribution (e.g., Gauss) to model the prediction error, which hinders sufficient network training. In this article, we propose a novel framework called DRPose to revise the abovementioned deviations. Specifically, to address the representation deviation, a scale-aware domain bridging (SDB) block is proposed to transfer feature maps from multiple scale-dependent domains into a unified intermediate domain with dynamic parameters. To address the target deviation, a differentiable coordinate decoder (DCD) is presented to adaptively adjust target distribution of heatmaps in an end-to-end manner. Extensive experiments show that the proposed method significantly improves the performance of most existing models with negligible additional cost. Beyond this, our method achieves 77.1% AP on the COCO test-dev set, outperforming prior works with similar model complexity.