RMPE: Regional Multi-person Pose Estimation

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 ICCV 2017 PDF ICCV 2017 Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Keypoint Detection COCO AlphaPose Test AP 73.3 # 6
FPS 23 # 1
Pose Estimation COCO test-dev RMPE++ AP 72.3 # 9
AP50 89.2 # 9
AP75 79.1 # 10
APL 78.6 # 7
APM 68.0 # 8
Pose Estimation COCO test-dev RMPE AP 61.8 # 14
AP50 83.7 # 14
AP75 69.8 # 13
APL 67.6 # 15
APM 58.6 # 10
Keypoint Detection COCO test-dev AlphaPose APL 81.5 # 3
Multi-Person Pose Estimation COCO test-dev RMPE AP 61.8 # 11
APL 67.6 # 8
APM 58.6 # 8
AP50 83.7 # 8
AP75 69.8 # 7
Multi-Person Pose Estimation MPII Multi-Person AlphaPose AP 82.1% # 1
Keypoint Detection MPII Multi-Person AlphaPose mAP@0.5 82.1% # 1

Methods used in the Paper


METHOD TYPE
Spatial Transformer
Image Model Blocks