  Given one reference facial image and a piece of speech as input, talking head
generation aims to synthesize a realistic-looking talking head video. However,
generating a lip-synchronized video with natural head movements is challenging.
The same speech clip can generate multiple possible lip and head movements,
that is, there is no one-to-one mapping relationship between them. To overcome
this problem, we propose a Speech Feature Extractor (SFE) based on
memory-augmented self-supervised contrastive learning, which introduces the
memory module to store multiple different speech mapping results. In addition,
we introduce the Mixed Density Networks (MDN) into the landmark regression task
to generate multiple predicted facial landmarks. Extensive qualitative and
quantitative experiments show that the quality of our facial animation is
significantly superior to that of the state-of-the-art (SOTA). The code has
been released at https://github.com/Yaxinzhao97/MACL.git.
