RMPE: Regional Multi-person Pose Estimation

ICCV 2017 Hao-Shu FangShuqin XieYu-Wing TaiCewu Lu

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... (read more)

PDF Abstract

Evaluation results from the paper

Task Dataset Model Metric name Metric value Global rank Compare
Multi-Person Pose Estimation MPII Multi-Person Regional Multi-Person Pose Estimation AP 82.1% # 1