  The task of lip synchronization (lip-sync) seeks to match the lips of human
faces with different audio. It has various applications in the film industry as
well as for creating virtual avatars and for video conferencing. This is a
challenging problem as one needs to simultaneously introduce detailed,
realistic lip movements while preserving the identity, pose, emotions, and
image quality. Many of the previous methods trying to solve this problem suffer
from image quality degradation due to a lack of complete contextual
information. In this paper, we present Diff2Lip, an audio-conditioned
diffusion-based model which is able to do lip synchronization in-the-wild while
preserving these qualities. We train our model on Voxceleb2, a video dataset
containing in-the-wild talking face videos. Extensive studies show that our
method outperforms popular methods like Wav2Lip and PC-AVS in Fr\'echet
inception distance (FID) metric and Mean Opinion Scores (MOS) of the users. We
show results on both reconstruction (same audio-video inputs) as well as cross
(different audio-video inputs) settings on Voxceleb2 and LRW datasets. Video
results and code can be accessed from our project page (
https://soumik-kanad.github.io/diff2lip ).
