  The one-shot talking-head synthesis task aims to animate a source image to
another pose and expression, which is dictated by a driving frame. Recent
methods rely on warping the appearance feature extracted from the source, by
using motion fields estimated from the sparse keypoints, that are learned in an
unsupervised manner. Due to their lightweight formulation, they are suitable
for video conferencing with reduced bandwidth. However, based on our study,
current methods suffer from two major limitations: 1) unsatisfactory generation
quality in the case of large head poses and the existence of observable pose
misalignment between the source and the first frame in driving videos. 2) fail
to capture fine yet critical face motion details due to the lack of semantic
understanding and appropriate face geometry regularization. To address these
shortcomings, we propose a novel method that leverages the rich face prior
information, the proposed model can generate face videos with improved semantic
consistency (improve baseline by $7\%$ in average keypoint distance) and
expression-preserving (outperform baseline by $15 \%$ in average emotion
embedding distance) under equivalent bandwidth. Additionally, incorporating
such prior information provides us with a convenient interface to achieve
highly controllable generation in terms of both pose and expression.

Computer Vision and Pattern Recognition;Artificial Intelligence