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We present a method for recovering the dense 3D surface of the hand byregressing the vertex coordinates of a mesh model from a single depth map. Tothis end, we use a two-stage 2D fully convolutional network architecture. Inthe first stage, the network estimates a dense correspondence field for everypixel on the depth map or image grid to the mesh grid. In the second stage, wedesign a differentiable operator to map features learned from the previousstage and regress a 3D coordinate map on the mesh grid. Finally, we sample fromthe mesh grid to recover the mesh vertices, and fit it an articulated templatemesh in closed form. During inference, the network can predict all the meshvertices, transformation matrices for every joint and the joint coordinates ina single forward pass. When given supervision on the sparse key-pointcoordinates, our method achieves state-of-the-art accuracy on NYU dataset forkey point localization while recovering mesh vertices and a densecorrespondence map. Our framework can also be learned through self-supervisionby minimizing a set of data fitting and kinematic prior terms. Withmulti-camera rig during training to resolve self-occlusion, it can performcompetitively with strongly supervised methods Without any human annotation.

Dual Grid Net: hand mesh vertex regression from single depth maps