  We leverage the modern advancements in talking head generation to propose an
end-to-end system for talking head video compression. Our algorithm transmits
pivot frames intermittently while the rest of the talking head video is
generated by animating them. We use a state-of-the-art face reenactment network
to detect key points in the non-pivot frames and transmit them to the receiver.
A dense flow is then calculated to warp a pivot frame to reconstruct the
non-pivot ones. Transmitting key points instead of full frames leads to
significant compression. We propose a novel algorithm to adaptively select the
best-suited pivot frames at regular intervals to provide a smooth experience.
We also propose a frame-interpolater at the receiver's end to improve the
compression levels further. Finally, a face enhancement network improves
reconstruction quality, significantly improving several aspects like the
sharpness of the generations. We evaluate our method both qualitatively and
quantitatively on benchmark datasets and compare it with multiple compression
techniques. We release a demo video and additional information at
https://cvit.iiit.ac.in/research/projects/cvit-projects/talking-video-compression.
