  In many applications, synchronizing audio with visuals is crucial, such as in
creating graphic animations for films or games, translating movie audio into
different languages, and developing metaverse applications. This review
explores various methodologies for achieving realistic facial animations from
audio inputs, highlighting generative and adaptive models. Addressing
challenges like model training costs, dataset availability, and silent moment
distributions in audio data, it presents innovative solutions to enhance
performance and realism. The research also introduces a new taxonomy to
categorize audio-visual synchronization methods based on logistical aspects,
advancing the capabilities of virtual assistants, gaming, and interactive
digital media.
