用大模型让虚拟人拥有真实个性，提升元宇宙互动体验
Integrating Personality into Digital Humans: A Review of LLM-Driven Approaches for Virtual Reality
- 通过零样本、少样本和微调等方法赋予虚拟人个性化特征
- 结合表情与动作实现多模态情感表达，增强沉浸感
- 适合教育、心理治疗及游戏领域开发者参考
将大语言模型（LLMs）引入虚拟现实（VR）环境，为打造更具沉浸感和交互性的数字人类开辟新路径。借助LLM的生成能力及面部表情、手势等多模态输出，虚拟代理可模拟类人性格与情绪，显著提升用户体验。本文系统综述了使数字人类具备细腻人格特质的方法，涵盖零样本、少样本和微调策略。同时指出当前挑战：计算资源需求高、延迟问题突出，且缺乏标准化的多模态交互评估框架。解决这些瓶颈有助于推动教育、心理治疗和游戏等领域的应用发展，并促进跨学科协作，重塑VR中的人机交互模式。
原文摘要 · Abstract (English)
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.
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