@inproceedings{zhao-etal-2025-urbanvideo,
    title = "{U}rban{V}ideo-Bench: Benchmarking Vision-Language Models on Embodied Intelligence with Video Data in Urban Spaces",
    author = "Zhao, Baining  and
      Fang, Jianjie  and
      Dai, Zichao  and
      Wang, Ziyou  and
      Zha, Jirong  and
      Zhang, Weichen  and
      Gao, Chen  and
      Wang, Yue  and
      Cui, Jinqiang  and
      Chen, Xinlei  and
      Li, Yong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1558/",
    pages = "32400--32423",
    ISBN = "979-8-89176-251-0",
    abstract = "Large multimodal models exhibit remarkable intelligence, yet their embodied cognitive abilities during motion in open-ended urban aerial spaces remain to be explored. We introduce a benchmark to evaluate whether video-large language models (Video-LLMs) can naturally process continuous first-person visual observations like humans, enabling recall, perception, reasoning, and navigation. We have manually control drones to collect 3D embodied motion video data from real-world cities and simulated environments, resulting in 1.5k video clips. Then we design a pipeline to generate 5.2k multiple-choice questions. Evaluations of 17 widely-used Video-LLMs reveal current limitations in urban embodied cognition. Correlation analysis provides insight into the relationships between different tasks, showing that causal reasoning has a strong correlation with recall, perception, and navigation, while the abilities for counterfactual and associative reasoning exhibit lower correlation with other tasks. We also validate the potential for Sim-to-Real transfer in urban embodiment through fine-tuning."
}