  We propose a neural talking-head video synthesis model and demonstrate its
application to video conferencing. Our model learns to synthesize a
talking-head video using a source image containing the target person's
appearance and a driving video that dictates the motion in the output. Our
motion is encoded based on a novel keypoint representation, where the
identity-specific and motion-related information is decomposed unsupervisedly.
Extensive experimental validation shows that our model outperforms competing
methods on benchmark datasets. Moreover, our compact keypoint representation
enables a video conferencing system that achieves the same visual quality as
the commercial H.264 standard while only using one-tenth of the bandwidth.
Besides, we show our keypoint representation allows the user to rotate the head
during synthesis, which is useful for simulating face-to-face video
conferencing experiences.
