Enhancing Lip Synchronization in Deep Learning Models: An Evaluation of Supplementary Metrics for Wav2Lip Performance Optimization

Abstract

The technology of lip synchronization aims at lip movements in videos with corresponding audio and has proven itself to be extremely useful in multimedia applications. The Wav2Lip model leverages deep learning to achieve high-quality lip-syncing videos that have become a leading approach in this field. This paper investigates the use of different evaluation metrics in assessing the performance of the Wav2Lip model. The purpose of this analysis is to improve the loss metric in training the loss function in training the model and provide insights into improving the development of lip synchronization models for more realistic results.