  Speech-driven facial animation is useful for a variety of applications such
as telepresence, chatbots, etc. The necessary attributes of having a realistic
face animation are 1) audio-visual synchronization (2) identity preservation of
the target individual (3) plausible mouth movements (4) presence of natural eye
blinks. The existing methods mostly address the audio-visual lip
synchronization, and few recent works have addressed the synthesis of natural
eye blinks for overall video realism. In this paper, we propose a method for
identity-preserving realistic facial animation from speech. We first generate
person-independent facial landmarks from audio using DeepSpeech features for
invariance to different voices, accents, etc. To add realism, we impose eye
blinks on facial landmarks using unsupervised learning and retargets the
person-independent landmarks to person-specific landmarks to preserve the
identity-related facial structure which helps in the generation of plausible
mouth shapes of the target identity. Finally, we use LSGAN to generate the
facial texture from person-specific facial landmarks, using an attention
mechanism that helps to preserve identity-related texture. An extensive
comparison of our proposed method with the current state-of-the-art methods
demonstrates a significant improvement in terms of lip synchronization
accuracy, image reconstruction quality, sharpness, and identity-preservation. A
user study also reveals improved realism of our animation results over the
state-of-the-art methods. To the best of our knowledge, this is the first work
in speech-driven 2D facial animation that simultaneously addresses all the
above-mentioned attributes of a realistic speech-driven face animation.
