TEXT2VIDEO: TEXT-DRIVEN TALKING-HEAD VIDEO SYNTHESIS WITH PERSONALIZED PHONEME - POSE DICTIONARY

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 about 1 min of the training data, which is significantly less than audio-driven approaches; 2) It is more flexible and not subject to vulnerability due to speaker variation; 3) It significantly reduces the preprocessing and training time from several days for audio-based methods to 4 hours, which is 10 times faster. 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.