VFM in Your Hands: Optimizing Real-Time Scene Understanding for Mobile Augmented Reality

Mobile Augmented Reality (AR) applications demand high-quality, real-time visual prediction, including pixel-level depth and semantics, to enable immersive and context-aware user experiences. Recently, Vision Foundation Models (VFMs) have offered strong generalization capabilities on diverse and unseen data, supporting scalable mobile AR experiences. However, deploying VFMs on mobile devices is challenging due to computational limitations, particularly in maintaining both prediction accuracy and real-time performance. In this article, we present ARIA [3], the first system that enables on-device inference acceleration of a VFM. ARIA employs the heterogeneity of mobile processors through a parallel and selective inference scheme: full-frame prediction is periodically offloaded to a processor with high parallelism capability like GPU, while lowlatency updates on dynamic regions are conducted via a specialized accelerator like NPU. Implemented and evaluated using mobile devices, ARIA achieved significant improvements in accuracy and deadline success rate on real-world mobile AR scenarios.