Dense 3D Regression for Hand Pose Estimation

  title={Dense 3D Regression for Hand Pose Estimation},
  author={Chengde Wan and Thomas Probst and Luc Van Gool and Angela 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. This is achieved by careful design choices in pose parameterization, which leverages both 2D and 3D properties of depth map. Specifically, we decompose the pose parameters into a set of per-pixel estimations, i.e., 2D heat maps, 3D heat maps and unit 3D directional vector… CONTINUE READING
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