视频里的字，AI 还是写不对
视频生成模型已经能把画面做得像电影，但镜头里出现的文字——招牌、字幕、广告词——依然大面积出错。研究者造了一个专门考「视频里写字」的基准，300 个场景横跨广告、科普等五类，让 11 个最强模型去写，结果最好的一个单词错误率也有 25%，也就是说每四个词就错一个。他们还顺手搭了个「导演智能体」:让一个 AI 负责调度图像和视频生成，再拿视觉反馈反复改，这条路确实能把错误率压下来。它不是你明天能用上的东西，但告诉你一件事：AI 视频离「能当广告片用」还差在字上，而字恰恰是商业场景里最不能错的部分。
📄 原文摘要(英文)
Recent video generation models can produce highly realistic videos from natural language instructions, with visual quality approaching cinematic standards. Existing evaluation benchmarks, however, predominantly assess visual quality, aesthetic appeal and physical plausibility, while paying limited attention to text, an essential medium for conveying information in everyday scenes. A generated video may appear visually compelling and feature lifelike subjects, yet still render the text within the scene incorrectly. To address this overlooked dimension, we introduce VTR-Bench, a systematic benchmark for evaluating the Visual Text Rendering capabilities of video generation models. VTR-Bench situates text within concrete application scenarios, such as advertisements and scientific videos, with 300 carefully constructed prompts spanning five scenario categories. We develop an automated evaluation pipeline with human alignments that separately assesses text fidelity through carrier-specific transcription and scene and motion requirements through a prompt-specific chain of query. Beyond evaluation, we introduce a Keyframe-Guided Agentic Framework in which a Director agent coordinates image and video generation with visual evaluation, guiding iterative refinement and candidate selection through visual feedback. Experiments on 11 state-of-the-art models reveal widespread difficulties in accurately rendering scene text, with the best-performing model recording an overall word error rate (WER) of 0.250. We further analyze text rendering failures to characterize the challenges faced by current video generation models. These findings highlight visual text rendering as a key challenge for video generation and demonstrate a practical path toward improvement. Code is available at https://github.com/hardenyu21/VTR-Bench.