  In this paper, we propose a spatial-temporal relational reasoning networks
(STRRN) approach to investigate the problem of omni-supervised face alignment
in videos. Unlike existing fully supervised methods which rely on numerous
annotations by hand, our learner exploits large scale unlabeled videos plus
available labeled data to generate auxiliary plausible training annotations.
Motivated by the fact that neighbouring facial landmarks are usually correlated
and coherent across consecutive frames, our approach automatically reasons
about discriminative spatial-temporal relationships among landmarks for stable
face tracking. Specifically, we carefully develop an interpretable and
efficient network module, which disentangles facial geometry relationship for
every static frame and simultaneously enforces the bi-directional
cycle-consistency across adjacent frames, thus allowing the modeling of
intrinsic spatial-temporal relations from raw face sequences. Extensive
experimental results demonstrate that our approach surpasses the performance of
most fully supervised state-of-the-arts.
