With the advance of deep learning technology, automatic video generation from audio or text has become an emerging and promising research topic. In this paper, we present a novel approach to synthesize video from the text. The method builds a phoneme-pose dictionary and trains a generative adversarial network (GAN) to generate video from interpolated phoneme poses. Compared to audio-driven video generation algorithms, our approach has a number of advantages: 1) It only needs a fraction of the training data used by an audio-driven approach; 2) It is more flexible and not subject to vulnerability due to speaker variation; 3) It significantly reduces the preprocessing, training and inference time. We perform extensive experiments to compare the proposed method with state-of-the-art talking face generation methods on a benchmark dataset and datasets of our own. The results demonstrate the effectiveness and superiority of our approach.
@article{arxiv.2104.14631,
title = {Text2Video: Text-driven Talking-head Video Synthesis with Personalized Phoneme-Pose Dictionary},
author = {Sibo Zhang and Jiahong Yuan and Miao Liao and Liangjun Zhang},
journal= {arXiv preprint arXiv:2104.14631},
year = {2022}
}