  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.
