AdjustAR: AI-Driven In-Situ Adjustment of Site-Specific Augmented Reality Content

TL;DR

AdjustAR leverages multimodal large language models to automatically detect and correct misalignments in site-specific outdoor AR content in real-time, ensuring consistent user experience despite environmental changes.

Contribution

This paper introduces AdjustAR, a novel system that uses MLLMs for in-situ, automated correction of AR content alignment in dynamic outdoor environments.

Findings

Effective detection of misalignments using MLLMs

Real-time updates of AR content maintaining alignment

Improved user experience in changing environments

Abstract

Site-specific outdoor AR experiences are typically authored using static 3D models, but are deployed in physical environments that change over time. As a result, virtual content may become misaligned with its intended real-world referents, degrading user experience and compromising contextual interpretation. We present AdjustAR, a system that supports in-situ correction of AR content in dynamic environments using multimodal large language models (MLLMs). Given a composite image comprising the originally authored view and the current live user view from the same perspective, an MLLM detects contextual misalignments and proposes revised 2D placements for affected AR elements. These corrections are backprojected into 3D space to update the scene at runtime. By leveraging MLLMs for visual-semantic reasoning, this approach enables automated runtime corrections to maintain alignment with the authored intent as real-world target environments evolve.