Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided
  Role-playing Agents
Authors: name: Xueqiao Zhang, name: Chao Zhang, name: Jingtao Xu, name: Yifan Zhu, name: Xin Shi, name: Yi Yang, name: Yawei Luo

Role-playing agents (RPAs) have attracted growing interest for their ability
to simulate immersive and interactive characters. However, existing approaches
primarily focus on static role profiles, overlooking the dynamic perceptual
abilities inherent to humans. To bridge this gap, we introduce the concept of
dynamic role profiles by incorporating video modality into RPAs. To support
this, we construct Role-playing-Video60k, a large-scale, high-quality dataset
comprising 60k videos and 700k corresponding dialogues. Based on this dataset,
we develop a comprehensive RPA framework that combines adaptive temporal
sampling with both dynamic and static role profile representations.
Specifically, the dynamic profile is created by adaptively sampling video
frames and feeding them to the LLM in temporal order, while the static profile
consists of (1) character dialogues from training videos during fine-tuning,
and (2) a summary context from the input video during inference. This joint
integration enables RPAs to generate greater responses. Furthermore, we propose
a robust evaluation method covering eight metrics. Experimental results
demonstrate the effectiveness of our framework, highlighting the importance of
dynamic role profiles in developing RPAs.