  In this work, we explore a new problem of frame interpolation for speech
videos. Such content today forms the major form of online communication. We try
to solve this problem by using several deep learning video generation
algorithms to generate the missing frames. We also provide examples where
computer vision models despite showing high performance on conventional
non-linguistic metrics fail to accurately produce faithful interpolation of
speech. With this motivation, we provide a new set of linguistically-informed
metrics specifically targeted to the problem of speech videos interpolation. We
also release several datasets to test computer vision video generation models
of their speech understanding.
