Dense 3D Regression for Hand Pose Estimation

CVPR 2018 Chengde WanThomas ProbstLuc Van GoolAngela Yao

We present a simple and effective method for 3D hand pose estimation from a single depth frame. As opposed to previous state-of-the-art methods based on holistic 3D regression, our method works on dense pixel-wise estimation... (read more)

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Evaluation Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK COMPARE
Hand Pose Estimation ICVL Hands Dense Pixel-wise Estimation Average 3D Error 7.3 # 6
Hand Pose Estimation MSRA Hands Dense Pixel-wise Estimation Average 3D Error 7.2 # 2
Hand Pose Estimation NYU Hands Dense Pixel-wise Estimation Average 3D Error 10.2 # 5