Learning-based super-resolution of 3D face model

Super resolution technique could produce a higher resolution image than the originally captured one. However, nearly all super-resolution algorithms arm at 2D images. In this paper, we focus on generating the 3D face model of higher resolution from one input of 3D face model. In our method, the 3D face models firstly are all regularized via resampling in cylindrical representation. The super resolution then performs in the regular domain of cylindrical coordinate. The experiments using USF HumanID 3D face database of 137 3D face models are carried out, and demonstrate the presented algorithm is promising.