Globally-Robust Instance Identification and Locally-Accurate Keypoint Alignment for Multi-Person Pose Estimation

Fangzheng Tian, Sungchan Kim

Abstract: Scenes with a large number of human instances are characterized by significant overlap of the instances with similar appearance, occlusion, and scale variation. We propose GRAPE, a novel method that leverages both Globally Robust human instance identification and locally Accurate keypoint alignment for 2D Pose Estimation. GRAPE predicts instance center and keypoint heatmaps, as global identifications of instance location and scale, and keypoint offset vectors from instance centers, as representations of accurate local keypoint positions. We use Transformer to jointly learn the global and local contexts, which allows us to robustly detect instance centers even in difficult cases such as crowded scenes, and align instance offset vectors with relevant keypoint heatmaps, resulting in refined final poses. GRAPE also predicts keypoint visibility, which is crucial for estimating centers of partially visible instances in crowded scenes. We demonstrate that GRAPE achieves state-of-the-art performance on the CrowdPose, OCHuman, and COCO datasets. The benefit of GRAPE is more apparent on crowded scenes (CrowdPose and OCHuman), where our model significantly outperforms previous methods, especially on hard examples.