随着故事展开：像人类一样观看电影并识别角色
As the Story Unfolds: Watching a Film and Identifying Characters as a Human Does
AI总结：
该研究提出无需外部资源的角色命名任务，构建相关数据集与视频-对话模型，提出渐进式角色库框架，实现类人在线电影角色身份推断。
摘要：
电影在播放过程中会逐步引入角色。我们研究直接从电影本身识别主要角色并为其分配名字的问题，无需使用外部演员表、演员照片或预先构建的角色库。我们提出角色命名任务：给定对话中提及的一个名字及相关视频上下文，模型必须确定哪个可见角色对应该名字。该任务具有挑战性，因为名字解析通常依赖电影镜头结构（如正反打镜头）、自我介绍、直接称呼、第三方提及、目光接触和时间连续性等线索。我们提供用于评估角色命名和首次角色出现的数据集及标注，开发了一种视频-对话模型用于该任务，并将其与专有多模态大语言模型进行比较。我们还提出一种渐进式框架，用于在电影播放过程中构建角色库。这些贡献共同支持类人的在线电影理解，其中角色身份从电影本身推断得出。
英文摘要：
Films introduce characters incrementally as they progress. We study the problem of identifying principal characters and assigning their names directly from the movie itself, without using external cast lists, actor photographs, or pre-built character banks. We introduce the character naming task: given a name mentioned in dialogue and the associated video context, the model must determine which visible character bears that name. This task is challenging because name resolution often depends on cinematic cues such as shot-reverse-shot structure, self-introduction, direct address, third-party reference, gaze, and temporal continuity. We provide a dataset and annotations for evaluating character naming and first character appearances, develop a video-and-dialogue model for the task, and compare it with proprietary multimodal large language models. We further introduce a progressive framework for building a character bank as the film unfolds. Together, these contributions support online, human-like movie understanding in which character identities are inferred from the film itself.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
