Vivar: A Generative AR System for Intuitive Multi-Modal Sensor Data Presentation

2024-12-18

arXiv.org

AI Summary

This work addresses the challenge of interpreting multimodal sensor data for non-experts—stemming from its inherent complexity, cross-modal semantic gap, and dynamic time-varying nature—by proposing the first generative augmented reality (AR) system tailored for domain-agnostic users. Methodologically: (1) it introduces centroid interpolation-based cross-modal embedding, mapping raw sensor data into a pretrained visual latent space; (2) it designs an end-to-end AR scene generation framework requiring no domain-specific knowledge; and (3) it incorporates latent variable reuse and caching to enhance efficiency. Technically, the system integrates cross-modal embedding, foundation model–driven generation, and 3D Gaussian Splatting (3DGS), achieving an 11× reduction in inference latency without compromising visual fidelity. A user study with 485 participants demonstrates statistically significant improvements over baselines in explanation accuracy, cross-scenario consistency, and real-world applicability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

Abstract

Understanding sensor data can be challenging for non-experts because of the complexity and unique semantic meanings of sensor modalities. This calls for intuitive and effective methods to present sensor information. However, creating intuitive sensor data visualizations presents three key challenges: the variability of sensor readings, gaps in domain comprehension, and the dynamic nature of sensor data. To address these issues, we develop Vivar, a novel AR system that integrates multi-modal sensor data and presents 3D volumetric content for visualization. In particular, we introduce a cross-modal embedding approach that maps sensor data into a pre-trained visual embedding space through barycentric interpolation. This allows for accurate and continuous integration of multi-modal sensor information. Vivar also incorporates sensor-aware AR scene generation using foundation models and 3D Gaussian Splatting (3DGS) without requiring domain expertise. In addition, Vivar leverages latent reuse and caching strategies to accelerate 2D and AR content generation. Our extensive experiments demonstrate that our system achieves 11$ imes$ latency reduction without compromising quality. A user study involving over 485 participants, including domain experts, demonstrates Vivar's effectiveness in accuracy, consistency, and real-world applicability, paving the way for more intuitive sensor data visualization.

Problem

• Addresses difficulty in understanding multi-modal sensor data for non-experts
• Overcomes challenges in visualizing dynamic and variable sensor readings
• Enhances AR-based sensor data presentation without domain expertise

Innovation

• Cross-modal embedding maps sensor data visually
• Sensor-aware AR generation with foundation models
• Latent reuse accelerates content generation significantly