Realistic Lip Animation from Speech for Unseen Subjects using Few-shot Cross-modal Learning
Swapna Agarwal, Dipanjan Das, Brojeshwar Bhowmick · 2020
Recent advances in Convolutional Neural Network (CNN) based approaches have been able to generate convincing talking heads. Personalization of such talking heads requires training of the model with a large number of examples of the target person. This is also time consuming. In this paper, we propose a meta-learning based few-shot approach for generating personalized 2D talking heads where the lip animation is driven by a given audio. The idea is that the model is meta-trained with a dataset consisting of a large variety of subjects' ethnicity and vocabulary. We show that our meta-trained model is then capable of generating realistic animation for previously unseen face and unseen audio when finetuned with only a few-shot examples for a very short time (180 seconds). Considering the fact that facial expressions driven by audio are mainly expressed through motion around lips, we restrict ourselves to animating lip only. We have done the experiments on two publicly available datasets: GRID and TCD-TIMIT and our own captured data of Asian people. Both qualitative and quantitative analysis show that animations generated by such meta-learned model surpasses the state-of-the-art methods both in terms of realism and time taken.