  Recent advances in deep generative models led to the development of neural
face video compression codecs that use an order of magnitude less bandwidth
than engineered codecs. These neural codecs reconstruct the current frame by
warping a source frame and using a generative model to compensate for
imperfections in the warped source frame. Thereby, the warp is encoded and
transmitted using a small number of keypoints rather than a dense flow field,
which leads to massive savings compared to traditional codecs. However, by
relying on a single source frame only, these methods lead to inaccurate
reconstructions (e.g. one side of the head becomes unoccluded when turning the
head and has to be synthesized). Here, we aim to tackle this issue by relying
on multiple source frames (views of the face) and present encouraging results.
