Wav2Lip-HR: Synthesising clear high-resolution talking head in the wild

Abstract

Talking head generation aims to synthesize a photo-realistic speaking video with accurate lip motion. While this field has attracted more attention in recent audio-visual researches, most existing methods do not achieve the simultaneous improvement of lip synchronization and visual quality. In this paper, we propose Wav2Lip-HR, a neural-based audio-driven high-resolution talking head generation method. With our technique, all required to generate a clear high-resolution lip sync talking video is an image/video of the target face and an audio clip of any speech. The primary benefit of our method is that it generates clear high-resolution videos with sufficient facial details, rather than the ones just be large-sized with less clarity. We first analyze key factors that limit the clarity of generated videos and then put forth several important solutions to address the problem, including data augmentation, model structure improvement and a more effective loss function. Finally, we employ several efficient metrics to evaluate the clarity of images generated by our proposed approach as well as several widely used metrics to evaluate lip-sync performance. Numerous experiments demonstrate that our method has superior performance on visual quality and lip synchronization when compared to other existing schemes.