EnVisionVR: A Scene Interpretation Tool for Visual Accessibility in Virtual Reality.

Effective visual accessibility in Virtual Reality (VR) is crucial for Blind and Low Vision (BLV) users. However, designing visual accessibility systems is challenging due to the complexity of 3D VR environments and the need for techniques that can be easily retrofitted into existing applications. While prior work has studied how to enhance or translate visual information, the advancement of Vision Language Models (VLMs) provides an exciting opportunity to advance the scene interpretation capability of current systems. This paper presents EnVisionVR, an accessibility tool for VR scene interpretation. Through a formative study of usability barriers, we confirmed the lack of visual accessibility features as a key barrier for BLV users of VR content and applications. In response, we used our findings from the formative study to inform the design and development of EnVisionVR, a novel visual accessibility system leveraging a VLM, voice input and multimodal feedback for scene interpretation and virtual object interaction in VR. An evaluation with 12 BLV users demonstrated that EnVisionVR significantly improved their ability to locate virtual objects, effectively supporting scene understanding and object interaction.

Publication Details

Journal
Apollo (University of Cambridge)
Published
2025-10-03
DOI
https://doi.org/10.17863/cam.121861
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

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article

EnVisionVR: A Scene Interpretation Tool for Visual Accessibility in Virtual Reality.

Apollo (University of Cambridge)
Genomics and Phylogenetic Studies
article

EnVisionVR: A Scene Interpretation Tool for Visual Accessibility in Virtual Reality.

article en

Abstract

Effective visual accessibility in Virtual Reality (VR) is crucial for Blind and Low Vision (BLV) users. However, designing visual accessibility systems is challenging due to the complexity of 3D VR environments and the need for techniques that can be easily retrofitted into existing applications. While prior work has studied how to enhance or translate visual information, the advancement of Vision Language Models (VLMs) provides an exciting opportunity to advance the scene interpretation capability of current systems. This paper presents EnVisionVR, an accessibility tool for VR scene interpretation. Through a formative study of usability barriers, we confirmed the lack of visual accessibility features as a key barrier for BLV users of VR content and applications. In response, we used our findings from the formative study to inform the design and development of EnVisionVR, a novel visual accessibility system leveraging a VLM, voice input and multimodal feedback for scene interpretation and virtual object interaction in VR. An evaluation with 12 BLV users demonstrated that EnVisionVR significantly improved their ability to locate virtual objects, effectively supporting scene understanding and object interaction.

Apollo (University of Cambridge)
Cambridge Trust, China Scholarship Council, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 47%
Genomics and Phylogenetic Studies
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EnVisionVR: A Scene Interpretation Tool for Visual Accessibility in Virtual Reality. · Apollo (University of Cambridge) (2025) | TGRS Research Map | TGRS