In this paper, we introduce NarrativePlay, a novel system that allows users
to role-play a fictional character and interact with other characters in
narratives such as novels in an immersive environment. We leverage Large
Language Models (LLMs) to generate human-like responses, guided by personality
traits extracted from narratives. The system incorporates auto-generated visual
display of narrative settings, character portraits, and character speech,
greatly enhancing user experience. Our approach eschews predefined sandboxes,
focusing instead on main storyline events extracted from narratives from the
perspective of a user-selected character. NarrativePlay has been evaluated on
two types of narratives, detective and adventure stories, where users can
either explore the world or improve their favorability with the narrative
characters through conversations.

利用大型语言模型生成人类般的回复，通过提取故事中的人物特点，引入 NarrativePlay 系统，允许用户在沉浸式环境中扮演虚构角色并与其他角色互动，自动生成故事情境、角色形象和对话，大大提升用户体验。该系统关注用户所选角色视角下从故事中提取的主要情节事件，并在两种类型的故事中进行评估：侦探故事和冒险故事，用户可以通过对话探索世界或改善与故事角色的关系。

叙事游戏：互动叙事理解

NarrativePlay: Interactive Narrative Understanding

Inspired by the remarkable advances in video analytics, research teams are
stepping towards a greater ambition -- movie understanding. However, compared
to those activity videos in conventional datasets, movies are significantly
different. Generally, movies are much longer and consist of much richer
temporal structures. More importantly, the interactions among characters play a
central role in expressing the underlying story. To facilitate the efforts
along this direction, we construct a dataset called Movie Synopses Associations
(MSA) over 327 movies, which provides a synopsis for each movie, together with
annotated associations between synopsis paragraphs and movie segments. On top
of this dataset, we develop a framework to perform matching between movie
segments and synopsis paragraphs. This framework integrates different aspects
of a movie, including event dynamics and character interactions, and allows
them to be matched with parsed paragraphs, based on a graph-based formulation.
Our study shows that the proposed framework remarkably improves the matching
accuracy over conventional feature-based methods. It also reveals the
importance of narrative structures and character interactions in movie
understanding.

通过构建 327 个电影的电影简介关联（Movie Synopses Associations，MSA）数据集，以及开发一套能够识别电影片段并且和简介段落进行精准匹配的框架，本研究指出利用文学结构和人物互动来提高匹配精度是非常重要的，相较于传统的特征匹配方法，这种基于图形的综合匹配策略能够在电影理解领域有实质性的提升。