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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.16457 |
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| _version_ | 1866910053197414400 |
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| author | Brito, Iago Alves Dollis, Julia Soares Färber, Fernanda Bufon Ribeiro, Pedro Schindler Freire Brasil Sousa, Rafael Teixeira Filho, Arlindo Rodrigues Galvão |
| author_facet | Brito, Iago Alves Dollis, Julia Soares Färber, Fernanda Bufon Ribeiro, Pedro Schindler Freire Brasil Sousa, Rafael Teixeira Filho, Arlindo Rodrigues Galvão |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16457 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Personality into Digital Humans: A Review of LLM-Driven Approaches for Virtual Reality Brito, Iago Alves Dollis, Julia Soares Färber, Fernanda Bufon Ribeiro, Pedro Schindler Freire Brasil Sousa, Rafael Teixeira Filho, Arlindo Rodrigues Galvão Human-Computer Interaction Artificial Intelligence Computation and Language 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. |
| title | Integrating Personality into Digital Humans: A Review of LLM-Driven Approaches for Virtual Reality |
| topic | Human-Computer Interaction Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2503.16457 |