A Comprehensive Review and Taxonomy of Audio-Visual Synchronization Techniques for Realistic Speech Animation

TL;DR

This paper reviews audio-visual synchronization techniques for realistic speech animation, introduces a new taxonomy, and discusses challenges and solutions to improve virtual assistant and digital media applications.

Contribution

It provides a comprehensive taxonomy of synchronization methods and highlights innovative solutions to key challenges in the field.

Findings

Enhanced realism in facial animations from audio inputs

New taxonomy categorizes synchronization techniques effectively

Addresses training costs and dataset limitations

Abstract

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.