  Recently, there has been numerous breakthroughs in face hallucination tasks.
However, the task remains rather challenging in videos in comparison to the
images due to inherent consistency issues. The presence of extra temporal
dimension in video face hallucination makes it non-trivial to learn the facial
motion through out the sequence. In order to learn these fine spatio-temporal
motion details, we propose a novel cross-modal audio-visual Video Face
Hallucination Generative Adversarial Network (VFH-GAN). The architecture
exploits the semantic correlation of between the movement of the facial
structure and the associated speech signal. Another major issue in present
video based approaches is the presence of blurriness around the key facial
regions such as mouth and lips - where spatial displacement is much higher in
comparison to other areas. The proposed approach explicitly defines a lip
reading loss to learn the fine grain motion in these facial areas. During
training, GANs have potential to fit frequencies from low to high, which leads
to miss the hard to synthesize frequencies. Therefore, to add salient frequency
features to the network we add a frequency based loss function. The visual and
the quantitative comparison with state-of-the-art shows a significant
improvement in performance and efficacy.
