  Heatmap regression based face alignment has achieved prominent performance on
static images. However, the stability and accuracy are remarkably discounted
when applying the existing methods on dynamic videos. We attribute the
degradation to random noise and motion blur, which are common in videos. The
temporal information is critical to address this issue yet not fully considered
in the existing works. In this paper, we visit the video-oriented face
alignment problem in two perspectives: detection accuracy prefers lower error
for a single frame, and detection consistency forces better stability between
adjacent frames. On this basis, we propose a Jitter loss function that
leverages temporal information to suppress inaccurate as well as jittered
landmarks. The Jitter loss is involved in a novel framework with a fine-tuning
ConvLSTM structure over a backbone replaceable network. We further demonstrate
that accurate and stable landmarks are associated with different regions with
overlaps in a canonical coordinate, based on which the proposed Jitter loss
facilitates the optimization process during training. The proposed framework
achieves at least 40% improvement on stability evaluation metrics while
enhancing detection accuracy versus state-of-the-art methods. Generally, it can
swiftly convert a landmark detector for facial images to a better-performing
one for videos without retraining the entire model.
