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)

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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