  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 fields. The 2D/3D joint heat maps and 3D
joint offsets are estimated via multi-task network cascades, which is trained
end-to-end. The pixel-wise estimations can be directly translated into a vote
casting scheme. A variant of mean shift is then used to aggregate local votes
while enforcing consensus between the the estimated 3D pose and the pixel-wise
2D and 3D estimations by design. Our method is efficient and highly accurate.
On MSRA and NYU hand dataset, our method outperforms all previous
state-of-the-art approaches by a large margin. On the ICVL hand dataset, our
method achieves similar accuracy compared to the currently proposed nearly
saturated result and outperforms various other proposed methods. Code is
available $\href{"https://github.com/melonwan/denseReg"}{\text{online}}$.
