Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning

TL;DR

This paper introduces a new framework for arbitrary talking face generation that leverages audio-visual coherence through an asymmetric mutual information estimator and a dynamic attention mechanism to improve lip synchronization and facial motion realism.

Contribution

It proposes a novel audio-visual coherence learning approach with AMIE and a dynamic attention block, advancing lip-sync accuracy and facial motion smoothness in talking face synthesis.

Findings

Outperforms state-of-the-art on LRW and GRID datasets

Achieves high-resolution synthesis with gender and pose variations

Enhances lip synchronization through dynamic attention

Abstract

Talking face generation aims to synthesize a face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video via the given speech clip and facial image. Most existing methods mainly focus on either disentangling the information in a single image or learning temporal information between frames. However, cross-modality coherence between audio and video information has not been well addressed during synthesis. In this paper, we propose a novel arbitrary talking face generation framework by discovering the audio-visual coherence via the proposed Asymmetric Mutual Information Estimator (AMIE). In addition, we propose a Dynamic Attention (DA) block by selectively focusing the lip area of the input image during the training stage, to further enhance lip synchronization. Experimental results on benchmark LRW dataset and GRID dataset transcend the state-of-the-art methods on prevalent metrics with robust high-resolution synthesizing on gender and pose variations.