XaiR

Abstract

This paper discusses the integration of Multimodal Large Language Models (MLLMs) with Extended Reality (XR) headsets, focusing on enhancing machine understanding of physical spaces. By combining the contextual capabilities of MLLMs with the sensory inputs from XR, there is potential for more intuitive spatial interactions. However, the integration faces challenges due to the inherent limitations of MLLMs in processing 3D inputs and their significant resource demands for XR headsets. We introduce XaiR, a platform that facilitates integrating MLLMs with XR applications. XaiR uses a split architecture that offloads complex MLLM operations to a server while handling 3D world processing on the headset. This setup manages multiple input modalities, parallel models, and links them with real-time pose data, improving AR content placement in physical scenes. We tested XaiR’s effectiveness with a “cognitive assistant” application that guides users through tasks like making coffee or assembling furniture. Results from a 15-participant study show over 90% accuracy in task guidance and 85% accuracy in AR content anchoring. Additionally, we evaluate MLLMs against human operators for cognitive assistant tasks which provides insights into the quality of the captured data as well as the current gap in performance for cognitive assistant tasks.