Thanks to recent advancements in scalable deep architectures and large-scale pretraining, text-to-video generation has achieved unprecedented capabilities in producing high-fidelity, instruction-following content across a wide range of styles, enabling applications in advertising, entertainment, and education. However, these models' ability to render precise on-screen text, such as captions or mathematical formulas, remains largely untested, posing significant challenges for applications requiring exact textual accuracy. In this work, we introduce T2VTextBench, the first human-evaluation benchmark dedicated to evaluating on-screen text fidelity and temporal consistency in text-to-video models. Our suite of prompts integrates complex text strings with dynamic scene changes, testing each model's ability to maintain detailed instructions across frames. We evaluate ten state-of-the-art systems, ranging from open-source solutions to commercial offerings, and find that most struggle to generate legible, consistent text. These results highlight a critical gap in current video generators and provide a clear direction for future research aimed at enhancing textual manipulation in video synthesis.

本研究针对当前视频生成模型在准确呈现屏幕文本方面的不足，提出了T2VTextBench，这是第一个专门评估文本准确性和时间一致性的人类评估基准。通过测试十种最新系统的能力，发现大多数模型在生成清晰一致的文本方面存在显著挑战，为未来在视频合成中改善文本处理提供了明确的研究方向。

T2VTextBench：一种用于视频生成模型文本控制的人类评估基准