  Video generation is a challenging yet pivotal task in various industries,
such as gaming, e-commerce, and advertising. One significant unresolved aspect
within T2V is the effective visualization of text within generated videos.
Despite the progress achieved in Text-to-Video~(T2V) generation, current
methods still cannot effectively visualize texts in videos directly, as they
mainly focus on summarizing semantic scene information, understanding, and
depicting actions. While recent advances in image-level visual text generation
show promise, transitioning these techniques into the video domain faces
problems, notably in preserving textual fidelity and motion coherence. In this
paper, we propose an innovative approach termed Text-Animator for visual text
video generation. Text-Animator contains a text embedding injection module to
precisely depict the structures of visual text in generated videos. Besides, we
develop a camera control module and a text refinement module to improve the
stability of generated visual text by controlling the camera movement as well
as the motion of visualized text. Quantitative and qualitative experimental
results demonstrate the superiority of our approach to the accuracy of
generated visual text over state-of-the-art video generation methods. The
project page can be found at https://laulampaul.github.io/text-animator.html.
