  Multiple complex degradations are coupled in low-quality video faces in the
real world. Therefore, blind video face restoration is a highly challenging
ill-posed problem, requiring not only hallucinating high-fidelity details but
also enhancing temporal coherence across diverse pose variations. Restoring
each frame independently in a naive manner inevitably introduces temporal
incoherence and artifacts from pose changes and keypoint localization errors.
To address this, we propose the first blind video face restoration approach
with a novel parsing-guided temporal-coherent transformer (PGTFormer) without
pre-alignment. PGTFormer leverages semantic parsing guidance to select optimal
face priors for generating temporally coherent artifact-free results.
Specifically, we pre-train a temporal-spatial vector quantized auto-encoder on
high-quality video face datasets to extract expressive context-rich priors.
Then, the temporal parse-guided codebook predictor (TPCP) restores faces in
different poses based on face parsing context cues without performing face
pre-alignment. This strategy reduces artifacts and mitigates jitter caused by
cumulative errors from face pre-alignment. Finally, the temporal fidelity
regulator (TFR) enhances fidelity through temporal feature interaction and
improves video temporal consistency. Extensive experiments on face videos show
that our method outperforms previous face restoration baselines. The code will
be released on
\href{https://github.com/kepengxu/PGTFormer}{https://github.com/kepengxu/PGTFormer}.
