  Presenting high-resolution (HR) human appearance is always critical for the
human-centric videos. However, current imagery equipment can hardly capture HR
details all the time. Existing super-resolution algorithms barely mitigate the
problem by only considering universal and low-level priors of im-age patches.
In contrast, our algorithm is under bias towards the human body
super-resolution by taking advantage of high-level prior defined by HR human
appearance. Firstly, a motion analysis module extracts inherent motion pattern
from the HR reference video to refine the pose estimation of the low-resolution
(LR) sequence. Furthermore, a human body reconstruction module maps the HR
texture in the reference frames onto a 3D mesh model. Consequently, the input
LR videos get super-resolved HR human sequences are generated conditioned on
the original LR videos as well as few HR reference frames. Experiments on an
existing dataset and real-world data captured by hybrid cameras show that our
approach generates superior visual quality of human body compared with the
traditional method.
