ClickAIXR: On-Device Multimodal Vision-Language Interaction with Real-World Objects in Extended Reality

TL;DR

ClickAIXR is an on-device multimodal XR interaction framework that enables precise object selection and natural language querying, enhancing privacy and reducing latency compared to cloud-based systems.

Contribution

It introduces a novel on-device vision-language interaction system with click-based object selection in XR, improving privacy, transparency, and user experience.

Findings

User study shows acceptable latency and usability.

On-device inference enhances privacy and reduces reliance on cloud services.

System effectively combines click-based selection with natural language understanding.

Abstract

We present ClickAIXR, a novel on-device framework for multimodal vision-language interaction with objects in extended reality (XR). Unlike prior systems that rely on cloud-based AI (e.g., ChatGPT) or gaze-based selection (e.g., GazePointAR), ClickAIXR integrates an on-device vision-language model (VLM) with a controller-based object selection paradigm, enabling users to precisely click on real-world objects in XR. Once selected, the object image is processed locally by the VLM to answer natural language questions through both text and speech. This object-centered interaction reduces ambiguity inherent in gaze- or voice-only interfaces and improves transparency by performing all inference on-device, addressing concerns around privacy and latency. We implemented ClickAIXR in the Magic Leap SDK (C API) with ONNX-based local VLM inference. We conducted a user study comparing ClickAIXR with Gemini 2.5 Flash and ChatGPT 5, evaluating usability, trust, and user satisfaction. Results show that latency is moderate and user experience is acceptable. Our findings demonstrate the potential of click-based object selection combined with on-device AI to advance trustworthy, privacy-preserving XR interactions. The source code and supplementary materials are available at: nanovis.org/ClickAIXR.html