  One-shot talking head generation has received growing attention in recent
years, with various creative and practical applications. An ideal natural and
vivid generated talking head video should contain natural head pose changes.
However, it is challenging to map head pose sequences from driving audio since
there exists a natural gap between audio-visual modalities. In this work, we
propose a Flow-guided One-shot model that achieves NaTural head motions(FONT)
over generated talking heads. Specifically, the head pose prediction module is
designed to generate head pose sequences from the source face and driving
audio. We add the random sampling operation and the structural similarity
constraint to model the diversity in the one-to-many mapping between
audio-visual modality, thus predicting natural head poses. Then we develop a
keypoint predictor that produces unsupervised keypoints from the source face,
driving audio and pose sequences to describe the facial structure information.
Finally, a flow-guided occlusion-aware generator is employed to produce
photo-realistic talking head videos from the estimated keypoints and source
face. Extensive experimental results prove that FONT generates talking heads
with natural head poses and synchronized mouth shapes, outperforming other
compared methods.
