LAUSR

Regression based multi-person pose estimation receives increasing attention because of its promising potential in achieving realtime inference. However, the challenges in long-range 2D offset regression have restricted the regression accuracy, leading to a considerable performance gap compared with heatmap based methods. This paper tackles the challenge of long-range regression through simplifying the 2D offset regression to a classification task. We present a simple yet effective method, named PolarPose, to perform 2D regression in Polar coordinate. Through transforming the 2D offset regression in Cartesian coordinate to quantized orientation classification and 1D length estimation in the Polar coordinate, PolarPose effectively simplifies the regression task, making the framework easier to optimize. Moreover, to further boost the keypoint localization accuracy in PolarPose, we propose a multi-center regression to relieve the quantization error during orientation quantization. The resulting PolarPose framework is able to regress the keypoint offsets in a more reliable way, and achieves more accurate keypoint localization. Tested with the single-model and single-scale setting, PolarPose achieves the AP of 70.2% on COCO test-dev dataset, outperforming the state-of-the-art regression based methods. PolarPose also achieves promising efficiency, e.g., 71.5% AP at 21.5FPS and 68.5%AP at 24.2FPS and 65.5%AP at 27.2FPS on COCO val2017 dataset, faster than current state-of-the-art.