Talking Face Generation by Conditional Recurrent Adversarial Network

TL;DR

This paper introduces a novel conditional recurrent adversarial network for talking face generation that ensures smooth facial movements and accurate lip synchronization across diverse videos, outperforming existing methods.

Contribution

The work presents a new conditional video generation model incorporating temporal dependencies and a multi-task adversarial training scheme for improved realism and lip sync accuracy.

Findings

Outperforms state-of-the-art in visual quality and lip sync accuracy

Ensures smooth facial and lip movements over entire videos

Effective dataset reduction without quality loss

Abstract

Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip. Existing works either do not consider temporal dependency on face images across different video frames thus easily yielding noticeable/abrupt facial and lip movement or are only limited to the generation of talking face video for a specific person thus lacking generalization capacity. We propose a novel conditional video generation network where the audio input is treated as a condition for the recurrent adversarial network such that temporal dependency is incorporated to realize smooth transition for the lip and facial movement. In addition, we deploy a multi-task adversarial training scheme in the context of video generation to improve both photo-realism and the accuracy for lip synchronization. Finally, based on the phoneme distribution information extracted from the audio clip, we develop a sample selection method that effectively reduces the size of the training dataset without sacrificing the quality of the generated video. Extensive experiments on both controlled and uncontrolled datasets demonstrate the superiority of the proposed approach in terms of visual quality, lip sync accuracy, and smooth transition of lip and facial movement, as compared to the state-of-the-art.