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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

Mikhail Konenkov , Artem Lykov , Daria Trinitatova , Dzmitry Tsetserukou This is my paper
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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Cited by 3 Pith papers

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  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. 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.

  3. Analyzing Multimodal Interaction Strategies for LLM-Assisted Manipulation of 3D Scenes

    cs.HC 2024-10 unverdicted novelty 4.0 of 10

    Empirical study with 12 users identifies common interaction patterns and barriers when using LLMs for 3D scene manipulation in immersive settings and proposes design recommendations.

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