  Advances in technology have led to the development of methods that can create
desired visual multimedia. In particular, image generation using deep learning
has been extensively studied across diverse fields. In comparison, video
generation, especially on conditional inputs, remains a challenging and less
explored area. To narrow this gap, we aim to train our model to produce a video
corresponding to a given text description. We propose a novel training
framework, Text-to-Image-to-Video Generative Adversarial Network (TiVGAN),
which evolves frame-by-frame and finally produces a full-length video. In the
first phase, we focus on creating a high-quality single video frame while
learning the relationship between the text and an image. As the steps proceed,
our model is trained gradually on more number of consecutive frames.This
step-by-step learning process helps stabilize the training and enables the
creation of high-resolution video based on conditional text descriptions.
Qualitative and quantitative experimental results on various datasets
demonstrate the effectiveness of the proposed method.
