@inproceedings{yang-etal-2026-g,
    title = "{G}-Cap: A Game Character Caption Generator",
    author = "Yang, Yang  and
      Hu, Feng  and
      Zhang, Haiming  and
      Cheng, XU  and
      Zheng, Gui  and
      Yao, Liang  and
      Ren, Wenqi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.248/",
    pages = "5455--5473",
    ISBN = "979-8-89176-390-6",
    abstract = "While Large Vision-Language Models (LVLMs) have demonstrated remarkable proficiency in image captioning, existing research primarily focuses on real-world scenarios, leaving surreal, highly stylized, and semantically hybrid virtual-world scenarios significantly underexplored. In this work, we introduce \textbf{Game Character Captioning}, a novel task designed to evaluate LVLMs' capability to perceive and describe game character from the virtual-world. To facilitate evaluation, we establish \textbf{GC-Bench}, a manually annotated benchmark, and propose \textbf{Graph-F1} to effectively assess performance on this task. Our evaluation reveals that: (1) current state-of-the-art LVLMs, including closed-source giants such as and , struggle to maintain the high performance seen in real-world scenarios; and (2) a notable gap exists between open-source and closed-source models. To bridge this gap, we construct \textbf{GC-148K}, a large-scale dataset generated via a specialized data pipeline, and develop the \textbf{G-Cap} series. Experiments demonstrate that G-Cap series rivals the performance of advanced closed-source models at a lower cost, offering an efficient solution for industrial-grade production environment."
}