@inproceedings{bu-etal-2025-walk,
    title = "Walk in Others' Shoes with a Single Glance: Human-Centric Visual Grounding with Top-View Perspective Transformation",
    author = "Bu, Yuqi  and
      Wu, Xin  and
      Zhao, Zirui  and
      Cai, Yi  and
      Hsu, David  and
      Liu, Qiong",
    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.1306/",
    pages = "26904--26923",
    ISBN = "979-8-89176-251-0",
    abstract = "Visual perspective-taking, an ability to envision others' perspectives from a single self-perspective, is vital in human-robot interactions. Thus, we introduce a human-centric visual grounding task and a dataset to evaluate this ability. Recent advances in vision-language models (VLMs) have shown potential for inferring others' perspectives, yet are insensitive to information differences induced by slight perspective changes. To address this problem, we propose a top-view enhanced perspective transformation (TEP) method, which decomposes the transition from robot to human perspectives through an abstract top-view representation. It unifies perspectives and facilitates the capture of information differences from diverse perspectives. Experimental results show that TEP improves performance by up to 18{\%}, exhibits perspective-taking abilities across various perspectives, and generalizes effectively to robotic and dynamic scenarios."
}