  Under various poses and heavy occlusions,3D hand model reconstruction based
on a single monocular RGB image has been a challenging problem in computer
vision field for many years. In this paper, we propose a SR-Affine approach for
high-quality 3D hand model reconstruction. First, we propose an encoder-decoder
network architecture (AffineNet) for MANO hand reconstruction. Since MANO hand
is not detailed, we further propose SRNet to up-sampling point-clouds by image
super-resolution on the UV map. Many experiments demonstrate that our approach
is robust and outperforms the state-of-the-art methods on standard benchmarks,
including the FreiHAND and HO3D datasets.
