Photo-Realistic Talking Face Generation Under Latent Space Manipulation

Ridwan Salahudeen, Wan-Chi Siu, H. Anthony Chan

Abstract: This paper focuses on generating photo-realistic talking face videos by leveraging on semantic facial attributes in a latent space and capturing the talking style from an old video of a speaker. We formulate a process to manipulate facial attributes in the latent space by identifying semantic facial directions. We develop a deep learning pipeline to learn the correlation between the audio and the corresponding video frames from a reference video of a speaker in an aligned latent space. This correlation is used to navigate a static face image into frames of a talking face video, which is moderated by three carefully constructed loss functions, for accurate lip synchronization and photo-realistic video reconstruction. By combining these techniques, we aim to generate high-quality talking face videos that are visually realistic and synchronized with the provided audio input. Our results were evaluated against some state-of-the-art techniques on talking face generation, and we have recorded significant improvements in the image quality of the generated talking face video.

External IDs:dblp:journals/tce/SalahudeenSC25