  Talking face generation has been extensively investigated owing to its wide
applicability. The two primary frameworks used for talking face generation
comprise a text-driven framework, which generates synchronized speech and
talking faces from text, and a speech-driven framework, which generates talking
faces from speech. To integrate these frameworks, this paper proposes a unified
facial landmark generator (UniFLG). The proposed system exploits end-to-end
text-to-speech not only for synthesizing speech but also for extracting a
series of latent representations that are common to text and speech, and feeds
it to a landmark decoder to generate facial landmarks. We demonstrate that our
system achieves higher naturalness in both speech synthesis and facial landmark
generation compared to the state-of-the-art text-driven method. We further
demonstrate that our system can generate facial landmarks from speech of
speakers without facial video data or even speech data.
