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In recent years, heatmap regression based models have shown theireffectiveness in face alignment and pose estimation. However, ConventionalHeatmap Regression (CHR) is not accurate nor stable when dealing withhigh-resolution facial videos, since it finds the maximum activated location inheatmaps which are generated from rounding coordinates, and thus leads toquantization errors when scaling back to the original high-resolution space. Inthis paper, we propose a Fractional Heatmap Regression (FHR) forhigh-resolution video-based face alignment. The proposed FHR can accuratelyestimate the fractional part according to the 2D Gaussian function by samplingthree points in heatmaps. To further stabilize the landmarks among continuousvideo frames while maintaining the precise at the same time, we propose a novelstabilization loss that contains two terms to address time delay and non-smoothissues, respectively. Experiments on 300W, 300-VW and Talking Face datasetsclearly demonstrate that the proposed method is more accurate and stable thanthe state-of-the-art models.

Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos