Convolutional Pose Machines

Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation... (read more)

PDF Abstract CVPR 2016 PDF CVPR 2016 Abstract

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Pose Estimation FLIC Elbows Convolutional Pose Machines PCK@0.2 97.59% # 2
Pose Estimation FLIC Wrists Convolutional Pose Machines PCK@0.2 95.03% # 2
Pose Estimation Leeds Sports Poses Convolutional Pose Machines PCK 90.5% # 10
3D Human Pose Estimation Total Capture Tri-CPM Average MPJPE (mm) 99 # 8

Results from Other Papers


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK SOURCE PAPER COMPARE
Pose Estimation J-HMDB CPM Mean PCK@0.2 91.9 # 3
Pose Estimation MPII Human Pose Convolutional Pose Machines PCKh-0.5 88.52% # 27

Methods used in the Paper


METHOD TYPE
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