Convolutional Pose Machines

CVPR 2016 Shih-En WeiVarun RamakrishnaTakeo KanadeYaser Sheikh

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)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK LEADERBOARD
Pose Estimation FLIC Elbows Convolutional Pose Machines [email protected] 97.59% # 2
Pose Estimation FLIC Wrists Convolutional Pose Machines [email protected] 95.03% # 2
Pose Estimation Leeds Sports Poses Convolutional Pose Machines PCK 90.5% # 7
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 [email protected] 91.9 # 3
Pose Estimation MPII Human Pose Convolutional Pose Machines PCKh-0.5 88.52% # 21

Methods used in the Paper


METHOD TYPE
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