T2VWorldBench: A Benchmark for Evaluating World Knowledge in Text-to-Video Generation

Publication Date

5-5-2026

Document Type

Conference Proceeding

Publication Title

Proceedings 2026 IEEE Cvf Winter Conference on Applications of Computer Vision Wacv 2026

DOI

10.1109/WACV61042.2026.00626

First Page

6474

Last Page

6485

Abstract

Text-to-video (T2V) models have shown remarkable performance in generating visually reasonable scenes, while their capability to leverage world knowledge for ensuring semantic consistency and factual accuracy remains largely understudied. In response to this challenge, we propose T2VWorldBench, the first systematic evaluation framework for evaluating the world knowledge generation abilities of text-to-video models, covering 6 major categories, 60 subcategories, and 1,200 prompts across a wide range of domains, including physics, nature, activity, culture, causality, and object. To address both human preference and scalable evaluation, our benchmark incorporates both human evaluation and automated evaluation using vision-language models (VLMs). We evaluated the 10 most advanced text-to-video models currently available, ranging from open source to commercial models, and found that most models are unable to understand world knowledge and generate truly correct videos. These findings point out a critical gap in the capability of current text-to-video models to leverage world knowledge, providing valuable research opportunities and entry points for constructing models with robust capabilities for commonsense reasoning and factual generation. All the evaluation prompts and code can be found in https://github.com/magiclinux/world-knowledge.

Keywords

benchmarking, generative models (for video), text-to-video, world knowledge

Department

Information Systems and Technology

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