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
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Multi-Person Pose Estimation COCO test-dev RMPE AP 61.8 # 6
APL 67.6 # 7
APM 58.6 # 6
AP50 83.7 # 6
AP75 69.8 # 5
Multi-Person Pose Estimation MPII Multi-Person Regional Multi-Person Pose Estimation AP 82.1% # 1

Methods used in the Paper


METHOD TYPE
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