FT2TF: First-Person Statement Text-to-Talking Face Generation

Xingjian Diao, Ming Cheng, Wayner Barrios, SouYoung Jin

Abstract: Talking face generation has gained immense popularity in the computer vision community, with various applications including AR, VR, teleconferencing, digital assistants, and avatars. Traditional methods are mainly audio-driven, which have to deal with the inevitable resource-intensive nature of audio storage and processing. To address such a challenge, we propose FT2TF - First-Person Statement Text- To -Talking Face Generation, a novel one-stage end-to-end pipeline for talking face generation driven by first-person statement text. Different from previous work, our model only leverages visual and textual information without any other sources (e.g., audio/landmark/pose) during inference. Extensive experiments are conducted on LRS2 and LRS3 datasets, and results on multidimensional evaluation metrics are reported. Both quantitative and qual-itative results showcase that FT2TF outperforms existing relevant methods and reaches the state-of-the-art. This achievement highlights our model's capability to bridge first-person statements and dynamic face generation, providing insightful guidance for future work.