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.

本文提出了一种空间-时间关系推理网络 (STRRN) 的方法，用于研究视频中全方位监督的人脸对齐问题，该方法利用大规模无标注视频和可用的带标注数据生成辅助训练注释，自动推理人脸关键点间的关系以稳定面部跟踪，实验结果证明其性能优于现有的大多全监督艺术状态。

实现大规模未标注视频中全监督人脸对齐