arXiv subjects

Xianhao Carton Liu

Publications and source records attributed to Xianhao Carton Liu.

4 recordsLinked to original sources

arXiv2026

Generative Proxy: Synthesizing Proxy-Based Interfaces for Real-World Interaction Across AR Glasses

Interacting with real-world objects in AR is difficult, especially when targets are distant, cluttered, or occluded. These challenges are amplified on emerging lightweight AR glasses, which often lack binocular or large field of view on display, but also continuous inputs, such as hand or eye tracking. Proxy-based interfaces offer an alternative by allowing users to interact with virtual abstractions of physical objects that can be repositioned, reorganized, and adapted to the task and device. However, designing such interfaces is currently manual and highly device-specific. We present Generative Proxy, a method for automatically generating proxy-based interfaces from three specifications: scene, intent, and device capabilities. We formulate generation as a constrained synthesis problem that first produces valid interfaces for the target device and task, then ranks candidates using semantic and articulatory distance inspired by direct manipulation theory. We demonstrate Generative Proxy across diverse scenes, device profiles, and user intents. Expert evaluation shows initial evidence that generated proxy UIs are useful and usable, highlighting proxy-based abstraction as a promising interaction paradigm for future AR glasses.

cs.HC↗

arXiv2026

Can AR Embedded Visualizations Foster Appropriate Reliance on AI in Spatial Decision-Making? A Comparative Study of AR X-Ray vs. 2D Minimap

Artificial Intelligence (AI) and indoor sensing increasingly support decision-making in spatial environments. However, traditional visualization methods impose a substantial mental workload when viewers translate this digital information into real-world spaces, leading to inappropriate reliance on AI. Embedded visualizations in Augmented Reality (AR), by integrating information into physical environments, may reduce this workload and foster more appropriate reliance on AI. To assess this, we conducted an empirical study (N = 32) comparing an AR embedded visualization (X-ray) and 2D Minimap in AI-assisted, time-critical spatial target selection tasks. Surprisingly, evidence shows that the embedded visualization led to greater inappropriate reliance on AI, primarily as over-reliance, due to factors like perceptual challenges, visual proximity illusions, and highly realistic visual representations. Nonetheless, the embedded visualization demonstrated benefits in spatial mapping. We conclude by discussing empirical insights, design implications, and directions for future research on human-AI collaborative decision in AR.

cs.HC↗

arXiv2025

Vitessce Link: A Mixed Reality and 2D Display Hybrid Approach for Visual Analysis of 3D Tissue Maps

Advances in spatial omics and high-resolution imaging enable the creation of three-dimensional (3D) tissue maps that capture cellular organization and interactions in situ. While these data provide critical insights into tissue function and disease, their exploration is often constrained by tools limited to 2D displays or stereoscopic rendering without analytical integration. We present Vitessce Link, a web-based hybrid framework that unites a 3D stereoscopic view in mixed reality with a synchronized 2D display environment. Users can navigate volumetric data with intuitive hand gestures while controlling channels, filters, and derived data views through the Vitessce platform. Built on open standards and running entirely in the browser, Vitessce Link minimizes friction, supports integration with computational notebooks, and synchronizes interactions across devices via a lightweight WebSocket architecture. Case studies in nephrology and oncology demonstrate how the hybrid approach enhances segmentation evaluation, distance measurement, and interpretation of spatial relationships. Vitessce Link establishes a paradigm for integrative, web-native analysis of 3D tissue maps.

cs.HC↗

arXiv2025

Reality Proxy: Fluid Interactions with Real-World Objects in MR via Abstract Representations

Interacting with real-world objects in Mixed Reality (MR) often proves difficult when they are crowded, distant, or partially occluded, hindering straightforward selection and manipulation. We observe that these difficulties stem from performing interaction directly on physical objects, where input is tightly coupled to their physical constraints. Our key insight is to decouple interaction from these constraints by introducing proxies-abstract representations of real-world objects. We embody this concept in Reality Proxy, a system that seamlessly shifts interaction targets from physical objects to their proxies during selection. Beyond facilitating basic selection, Reality Proxy uses AI to enrich proxies with semantic attributes and hierarchical spatial relationships of their corresponding physical objects, enabling novel and previously cumbersome interactions in MR - such as skimming, attribute-based filtering, navigating nested groups, and complex multi object selections - all without requiring new gestures or menu systems. We demonstrate Reality Proxy's versatility across diverse scenarios, including office information retrieval, large-scale spatial navigation, and multi-drone control. An expert evaluation suggests the system's utility and usability, suggesting that proxy-based abstractions offer a powerful and generalizable interaction paradigm for future MR systems.

cs.HC↗