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VR-GPT: Visual Language Model for Intelligent Virtual Reality Applications

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arxiv 2405.11537 v3 pith:MDUHGJJQ submitted 2024-05-19 cs.RO cs.AIcs.ET

classification cs.ROcs.AIcs.ET
keywords userlanguagetaskvisualapplicationsinteractionrealitysystem
verification ladder T0 review T1 audit T2 compute T3 formal

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The advent of immersive Virtual Reality applications has transformed various domains, yet their integration with advanced artificial intelligence technologies like Visual Language Models remains underexplored. This study introduces a pioneering approach utilizing VLMs within VR environments to enhance user interaction and task efficiency. Leveraging the Unity engine and a custom-developed VLM, our system facilitates real-time, intuitive user interactions through natural language processing, without relying on visual text instructions. The incorporation of speech-to-text and text-to-speech technologies allows for seamless communication between the user and the VLM, enabling the system to guide users through complex tasks effectively. Preliminary experimental results indicate that utilizing VLMs not only reduces task completion times but also improves user comfort and task engagement compared to traditional VR interaction methods.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SpatialThinker: Reinforcing Scene Graph-Grounded Spatial Reasoning via Dense Rewards

    cs.CV 2025-11 conditional novelty 6.0 of 10

    Dense scene-graph-grounded rewards let a 7B multimodal LLM trained on 7K synthetic questions beat SFT and sparse-RL baselines and outscore GPT-4o on average across 12 spatial/real-world benchmarks.

  2. GesPrompt: Leveraging Co-Speech Gestures to Augment LLM-Based Interaction in Virtual Reality

    cs.HC 2025-05 conditional novelty 6.0 of 10

    GesPrompt lets VR users communicate spatial intent to LLM-based assistants through co-speech gestures and speech, reducing reliance on detailed verbal prompts in a small user study.

  3. Spatial 3D-LLM: Exploring Spatial Awareness in 3D Vision-Language Models

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Spatial 3D-LLM adds a progressive spatial awareness scheme to a 3D vision-language model, improving several 3D understanding and grounding metrics and introducing new distance and layout-editing tasks.

  4. Towards user-centered interactive medical image segmentation in VR with an assistive AI agent

    cs.HC 2025-05 conditional novelty 5.0 of 10

    SAMIRA lets users speak to a VR AI agent to segment brain and liver tumors, reaching high Dice accuracy and a System Usability Scale score near 90, with controller pointing slightly ahead of head and eye tracking.

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