Towards Real-world Video Face Restoration: A New Benchmark
CoRR(2024)
摘要
Blind face restoration (BFR) on images has significantly progressed over the
last several years, while real-world video face restoration (VFR), which is
more challenging for more complex face motions such as moving gaze directions
and facial orientations involved, remains unsolved. Typical BFR methods are
evaluated on privately synthesized datasets or self-collected real-world
low-quality face images, which are limited in their coverage of real-world
video frames. In this work, we introduced new real-world datasets named FOS
with a taxonomy of "Full, Occluded, and Side" faces from mainly video frames to
study the applicability of current methods on videos. Compared with existing
test datasets, FOS datasets cover more diverse degradations and involve face
samples from more complex scenarios, which helps to revisit current face
restoration approaches more comprehensively. Given the established datasets, we
benchmarked both the state-of-the-art BFR methods and the video super
resolution (VSR) methods to comprehensively study current approaches,
identifying their potential and limitations in VFR tasks. In addition, we
studied the effectiveness of the commonly used image quality assessment (IQA)
metrics and face IQA (FIQA) metrics by leveraging a subjective user study. With
extensive experimental results and detailed analysis provided, we gained
insights from the successes and failures of both current BFR and VSR methods.
These results also pose challenges to current face restoration approaches,
which we hope stimulate future advances in VFR research.
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