EPIC: A System Framework for Efficient Egocentric Perception on Embodied AR Glasses

Abstract

Modern smart AR glasses are evolving into intelligent systems that support foundation model-based assistance through continuous perception of the user and surrounding environment. However, this perception-first design creates major bottlenecks. Continuously capturing, processing, and storing rich perceptual streams, especially high-resolution egocentric video, imposes substantial power and memory overhead, which is difficult to sustain on resource-constrained AR glasses. In this work, we propose EPIC, an efficient egocentric perception system for embodied intelligence on smart AR glasses. EPIC is an algorithm-hardware co-optimization framework that leverages gaze, pose, and inertial signals to infer user intent and retain only the most informative parts of high-resolution perceptual input, greatly reducing perception overhead. Our results show that EPIC reduces memory footprint by $27.5\times$ and energy consumption by $24.3\times$ on average compared with full video baseline solution, while preserving intelligent assistance accuracy on egocentric video understanding tasks, a key application scenario for embodied intelligence on smart glasses.