Grounded GUI Understanding for Vision-Based Spatial Intelligent Agent: Exemplified by Extended Reality Apps

TL;DR

This paper introduces Orienter, a novel zero-shot framework for detecting interactable GUI elements in XR apps by understanding semantic context and iteratively refining detection, addressing challenges of heterogeneity and open-vocabulary categories.

Contribution

The paper presents the first zero-shot, context-sensitive IGE detection framework tailored for XR apps, improving detection accuracy over existing methods.

Findings

Orienter outperforms state-of-the-art detection approaches.

It effectively handles open-vocabulary and heterogeneous IGE categories.

The framework demonstrates robustness in complex XR environments.

Abstract

In recent years, spatial computing a.k.a. Extended Reality (XR) has emerged as a transformative technology, offering users immersive and interactive experiences across diversified virtual environments. Users can interact with XR apps through interactable GUI elements (IGEs) on the stereoscopic three-dimensional (3D) graphical user interface (GUI). The accurate recognition of these IGEs is instrumental, serving as the foundation of many software engineering tasks, including automated testing and effective GUI search. The most recent IGE detection approaches for 2D mobile apps typically train a supervised object detection model based on a large-scale manually-labeled GUI dataset, usually with a pre-defined set of clickable GUI element categories like buttons and spinners. Such approaches can hardly be applied to IGE detection in XR apps, due to a multitude of challenges including complexities posed by open-vocabulary and heterogeneous IGE categories, intricacies of context-sensitive interactability, and the necessities of precise spatial perception and visual-semantic alignment for accurate IGE detection results. Thus, it is necessary to embark on the IGE research tailored to XR apps. In this paper, we propose the first zero-shot cOntext-sensitive inteRactable GUI ElemeNT dEtection framework for virtual Reality apps, named Orienter. By imitating human behaviors, Orienter observes and understands the semantic contexts of XR app scenes first, before performing the detection. The detection process is iterated within a feedback-directed validation and reflection loop. Specifically, Orienter contains three components, including (1) Semantic context comprehension, (2) Reflection-directed IGE candidate detection, and (3) Context-sensitive interactability classification. Extensive experiments demonstrate that Orienter is more effective than the state-of-the-art GUI element detection approaches.