Integrating Personality into Digital Humans: A Review of LLM-Driven Approaches for Virtual Reality
It addresses the problem of creating more immersive digital humans for applications like education and gaming, but it is incremental as it focuses on reviewing existing approaches rather than introducing novel solutions.
This paper reviews methods for integrating personality traits into digital humans in virtual reality using large language models, aiming to enhance user experiences by simulating human-like interactions, but it does not present new experimental results or concrete numbers.
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.