RMPE: Regional Multi-Person Pose Estimation

Hao-Shu Fang, Shuqin Xie, Yu-Wing Tai, Cewu Lu; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2334-2343

Multi-person pose estimation in the wild is challenging. Although state-of-the-art human detectors have demonstrated good performance, small errors in localization and recognition are inevitable. These errors can cause failures for a single-person pose estimator (SPPE), especially for methods that solely depend on human detection results. In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes. Our framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). Our method is able to handle inaccurate bounding boxes and redundant detections, allowing it to achieve 76.7 mAP on the MPII (multi person) dataset. Our model and source codes are made publicly available.