Abstract
Current research efforts about passive sensing of 3D human hand pose have been hampered by the computational complexity caused by inverse kinematics and 3D reconstruction. We focuses on 3D hand posture estimation based on a single 2D image. We analyze some of its constraints to reduce the 27 DOFs to 12 DOFs without any significant degradation of performance. A novel algorithm to estimate the 3D hand posture from eight 2D projected feature points is proposed. Experimental results confirm that our algorithm gives good estimates of the 3D hand pose.
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