  In this paper, we investigate the task of hallucinating an authentic
high-resolution (HR) human face from multiple low-resolution (LR) video
snapshots. We propose a pure transformer-based model, dubbed VidFace, to fully
exploit the full-range spatio-temporal information and facial structure cues
among multiple thumbnails. Specifically, VidFace handles multiple snapshots all
at once and harnesses the spatial and temporal information integrally to
explore face alignments across all the frames, thus avoiding accumulating
alignment errors. Moreover, we design a recurrent position embedding module to
equip our transformer with facial priors, which not only effectively
regularises the alignment mechanism but also supplants notorious pre-training.
Finally, we curate a new large-scale video face hallucination dataset from the
public Voxceleb2 benchmark, which challenges prior arts on tackling unaligned
and tiny face snapshots. To the best of our knowledge, we are the first attempt
to develop a unified transformer-based solver tailored for video-based face
hallucination. Extensive experiments on public video face benchmarks show that
the proposed method significantly outperforms the state of the arts.
