CultureVidBench: Benchmarking Cultural Understanding in Text-to-Video Generation
cs.CV, cs.CL, cs.MM
Submitted: 2026-08-03
Updated: 2026-08-27
Comments: Project page:https://hanxjing.github.io/CultureVidBench/
Project page: https://hanxjing.github.io/CultureVidBench
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- World Simulation with Video Foundation Models for Physical AI
- Qwen3-VL Technical Report
- VideoPhy: Evaluating Physical Commonsense for Video Generation
- RoboTrustBench: Benchmarking the Trustworthiness of Video World Models for Robotic Manipulation
- "PhyWorldBench": A Comprehensive Evaluation of Physical Realism in Text-to-Video Models
- Towards World Simulator: Crafting Physical Commonsense-Based Benchmark for Video Generation
- LTX-2: Efficient Joint Audio-Visual Foundation Model
- VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
- Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation
- HunyuanVideo 1.5 Technical Report
- VideoNorms: Benchmarking Cultural Awareness of Video Language Models
- Wan: Open and Advanced Large-Scale Video Generative Models
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