  Recently, facial landmark detection algorithms have achieved remarkable
performance on static images. However, these algorithms are neither accurate
nor stable in motion-blurred videos. The missing of structure information makes
it difficult for state-of-the-art facial landmark detection algorithms to yield
good results. In this paper, we propose a framework named FAB that takes
advantage of structure consistency in the temporal dimension for facial
landmark detection in motion-blurred videos. A structure predictor is proposed
to predict the missing face structural information temporally, which serves as
a geometry prior. This allows our framework to work as a virtuous circle. On
one hand, the geometry prior helps our structure-aware deblurring network
generates high quality deblurred images which lead to better landmark detection
results. On the other hand, better landmark detection results help structure
predictor generate better geometry prior for the next frame. Moreover, it is a
flexible video-based framework that can incorporate any static image-based
methods to provide a performance boost on video datasets. Extensive experiments
on Blurred-300VW, the proposed Real-world Motion Blur (RWMB) datasets and 300VW
demonstrate the superior performance to the state-of-the-art methods. Datasets
and models will be publicly available at
https://keqiangsun.github.io/projects/FAB/FAB.html.

Computer Vision and Pattern Recognition