The integration of large language models (LLMs) into virtual reality (VR) environments has opened new pathways for creating more immersive and interactive digital humans. By leveraging the generative capabilities of LLMs alongside multimodal outputs such as facial expressions and gestures, virtual agents can simulate human-like personalities and emotions, fostering richer and more engaging user experiences. This paper provides a comprehensive review of methods for enabling digital humans to adopt nuanced personality traits, exploring approaches such as zero-shot, few-shot, and fine-tuning. Additionally, it highlights the challenges of integrating LLM-driven personality traits into VR, including computational demands, latency issues, and the lack of standardized evaluation frameworks for multimodal interactions. By addressing these gaps, this work lays a foundation for advancing applications in education, therapy, and gaming, while fostering interdisciplinary collaboration to redefine human-computer interaction in VR.

翻译：将大语言模型（LLMs）集成到虚拟现实（VR）环境中，为创建更具沉浸感和交互性的数字人开辟了新途径。通过利用LLMs的生成能力，并结合面部表情和手势等多模态输出，虚拟智能体能够模拟类人的人格与情感，从而营造更丰富、更具吸引力的用户体验。本文系统综述了使数字人具备细腻人格特质的方法，探讨了零样本、少样本及微调等多种技术路径。此外，文章重点分析了将LLMs驱动的人格特质融入VR所面临的挑战，包括计算需求、延迟问题以及多模态交互缺乏标准化评估框架等。通过探讨这些不足，本研究为推进教育、治疗和游戏等领域的应用奠定了基础，同时促进跨学科合作，以重新定义VR中的人机交互范式。