  Lip reading aims to recognize text from talking lip, while lip generation
aims to synthesize talking lip according to text, which is a key component in
talking face generation and is a dual task of lip reading. In this paper, we
develop DualLip, a system that jointly improves lip reading and generation by
leveraging the task duality and using unlabeled text and lip video data. The
key ideas of the DualLip include: 1) Generate lip video from unlabeled text
with a lip generation model, and use the pseudo pairs to improve lip reading;
2) Generate text from unlabeled lip video with a lip reading model, and use the
pseudo pairs to improve lip generation. We further extend DualLip to talking
face generation with two additionally introduced components: lip to face
generation and text to speech generation. Experiments on GRID and TCD-TIMIT
demonstrate the effectiveness of DualLip on improving lip reading, lip
generation, and talking face generation by utilizing unlabeled data.
Specifically, the lip generation model in our DualLip system trained with
only10% paired data surpasses the performance of that trained with the whole
paired data. And on the GRID benchmark of lip reading, we achieve 1.16%
character error rate and 2.71% word error rate, outperforming the
state-of-the-art models using the same amount of paired data.
