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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.03699 |
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| _version_ | 1866908479649742848 |
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| author | Peter, Subin Raj |
| author_facet | Peter, Subin Raj |
| contents | Virtual Reality (VR) has emerged as a powerful tool for workforce training, offering immersive, interactive, and risk-free environments that enhance skill acquisition, decision-making, and confidence. Despite its advantages, developing VR applications for training remains a significant challenge due to the time, expertise, and resources required to create accurate and engaging instructional content. To address these limitations, this paper proposes a novel approach that leverages Large Language Models (LLMs) to automate the generation of virtual instructions from textual input. The system comprises two core components: an LLM module that extracts task-relevant information from the text, and an intelligent module that transforms this information into animated demonstrations and visual cues within a VR environment. The intelligent module receives input from the LLM module and interprets the extracted information. Based on this, an instruction generator creates training content using relevant data from a database. The instruction generator generates the instruction by changing the color of virtual objects and creating animations to illustrate tasks. This approach enhances training effectiveness and reduces development overhead, making VR-based training more scalable and adaptable to evolving industrial needs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03699 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Text2VR: Automated instruction Generation in Virtual Reality using Large language Models for Assembly Task Peter, Subin Raj Computer Vision and Pattern Recognition Human-Computer Interaction Multimedia cs.MM Virtual Reality (VR) has emerged as a powerful tool for workforce training, offering immersive, interactive, and risk-free environments that enhance skill acquisition, decision-making, and confidence. Despite its advantages, developing VR applications for training remains a significant challenge due to the time, expertise, and resources required to create accurate and engaging instructional content. To address these limitations, this paper proposes a novel approach that leverages Large Language Models (LLMs) to automate the generation of virtual instructions from textual input. The system comprises two core components: an LLM module that extracts task-relevant information from the text, and an intelligent module that transforms this information into animated demonstrations and visual cues within a VR environment. The intelligent module receives input from the LLM module and interprets the extracted information. Based on this, an instruction generator creates training content using relevant data from a database. The instruction generator generates the instruction by changing the color of virtual objects and creating animations to illustrate tasks. This approach enhances training effectiveness and reduces development overhead, making VR-based training more scalable and adaptable to evolving industrial needs. |
| title | Text2VR: Automated instruction Generation in Virtual Reality using Large language Models for Assembly Task |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction Multimedia cs.MM |
| url | https://arxiv.org/abs/2508.03699 |