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Recently, there has been numerous breakthroughs in face hallucination tasks.However, the task remains rather challenging in videos in comparison to theimages due to inherent consistency issues. The presence of extra temporaldimension in video face hallucination makes it non-trivial to learn the facialmotion through out the sequence. In order to learn these fine spatio-temporalmotion details, we propose a novel cross-modal audio-visual Video FaceHallucination Generative Adversarial Network (VFH-GAN). The architectureexploits the semantic correlation of between the movement of the facialstructure and the associated speech signal. Another major issue in presentvideo based approaches is the presence of blurriness around the key facialregions such as mouth and lips - where spatial displacement is much higher incomparison to other areas. The proposed approach explicitly defines a lipreading loss to learn the fine grain motion in these facial areas. Duringtraining, GANs have potential to fit frequencies from low to high, which leadsto miss the hard to synthesize frequencies. Therefore, to add salient frequencyfeatures to the network we add a frequency based loss function. The visual andthe quantitative comparison with state-of-the-art shows a significantimprovement in performance and efficacy.

Audio-visual video face hallucination with frequency supervision and cross modality support by speech based lip reading loss