  In recent years, heatmap regression based models have shown their
effectiveness in face alignment and pose estimation. However, Conventional
Heatmap Regression (CHR) is not accurate nor stable when dealing with
high-resolution facial videos, since it finds the maximum activated location in
heatmaps which are generated from rounding coordinates, and thus leads to
quantization errors when scaling back to the original high-resolution space. In
this paper, we propose a Fractional Heatmap Regression (FHR) for
high-resolution video-based face alignment. The proposed FHR can accurately
estimate the fractional part according to the 2D Gaussian function by sampling
three points in heatmaps. To further stabilize the landmarks among continuous
video frames while maintaining the precise at the same time, we propose a novel
stabilization loss that contains two terms to address time delay and non-smooth
issues, respectively. Experiments on 300W, 300-VW and Talking Face datasets
clearly demonstrate that the proposed method is more accurate and stable than
the state-of-the-art models.

Computer Vision and Pattern Recognition