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 this https URL

我们提出了一种创新的方法 Text-Animator，用于视觉文本视频生成，通过精确描述生成视频中视觉文本的结构，并通过控制摄像机移动和文本运动来改善生成视觉文本的稳定性，实验证明了我们方法在生成视觉文本准确性上的优越性。

Text-Animator: 可控的视觉文本视频生成

Text-Animator: Controllable Visual Text Video Generation

Recently, there has been a growing interest in developing diffusion-based
text-to-image generative models capable of generating coherent and well-formed
visual text. In this paper, we propose a novel and efficient approach called
GlyphControl to address this task. Unlike existing methods that rely on
character-aware text encoders like ByT5 and require retraining of text-to-image
models, our approach leverages additional glyph conditional information to
enhance the performance of the off-the-shelf Stable-Diffusion model in
generating accurate visual text. By incorporating glyph instructions, users can
customize the content, location, and size of the generated text according to
their specific requirements. To facilitate further research in visual text
generation, we construct a training benchmark dataset called LAION-Glyph. We
evaluate the effectiveness of our approach by measuring OCR-based metrics and
CLIP scores of the generated visual text. Our empirical evaluations demonstrate
that GlyphControl outperforms the recent DeepFloyd IF approach in terms of OCR
accuracy and CLIP scores, highlighting the efficacy of our method.

该论文提出了一种名为 GlyphControl 的方法，用于增强现有的 Stable-Diffusion 模型在生成准确视觉文本方面的性能，有效地生成可定制的文本内容，使用自定义训练数据集 LAION-Glyph 进行评估，实验证明了该方法在 OCR 准确度方面优于 DeepFloyd IF 方法。