标题： 社会代理：通过对话大语言模型代理掌握二元非语言行为生成 显示英文标题

标题： Social Agent: Mastering Dyadic Nonverbal Behavior Generation via Conversational LLM Agents

摘要： 我们提出Social Agent，一种用于合成现实且符合语境的双人对话中的伴随言语非语言行为的新框架。 在该框架中，我们开发了一个由大型语言模型（LLM）驱动的代理系统，以引导对话流程并确定双方参与者的适当互动行为。 此外，我们提出了一种基于自回归扩散模型的新双人手势生成模型，该模型从语音信号中合成协调的动作。 代理系统的输出被转换为手势生成器的高层指导，从而在行为和动作层面产生逼真的运动。 此外，代理系统会定期检查对话者的动作并推断他们的意图，形成一个持续的反馈循环，使两名参与者之间能够进行动态和响应式的互动。 用户研究和定量评估表明，我们的模型显著提高了双人互动的质量，产生了自然、同步的非语言行为。 显示英文摘要

摘要： We present Social Agent, a novel framework for synthesizing realistic and contextually appropriate co-speech nonverbal behaviors in dyadic conversations. In this framework, we develop an agentic system driven by a Large Language Model (LLM) to direct the conversation flow and determine appropriate interactive behaviors for both participants. Additionally, we propose a novel dual-person gesture generation model based on an auto-regressive diffusion model, which synthesizes coordinated motions from speech signals. The output of the agentic system is translated into high-level guidance for the gesture generator, resulting in realistic movement at both the behavioral and motion levels. Furthermore, the agentic system periodically examines the movements of interlocutors and infers their intentions, forming a continuous feedback loop that enables dynamic and responsive interactions between the two participants. User studies and quantitative evaluations show that our model significantly improves the quality of dyadic interactions, producing natural, synchronized nonverbal behaviors.