Scaling Real-Time Video Synthesis: Optimizing Local Inference Engines for the Next Generation of AR Wearables
Table of Contents Introduction The Landscape of AR Wearables and Real‑Time Video Synthesis Core Challenges in Local Inference for Video Synthesis Architecture of Modern Inference Engines for Wearables Model‑Level Optimizations Efficient Data Pipelines & Memory Management Scheduling & Runtime Strategies Case Study: Real‑Time Neural Radiance Fields (NeRF) on AR Glasses Benchmarking & Metrics for Wearable Video Synthesis Future Directions Conclusion Resources Introduction Augmented reality (AR) wearables are moving from niche prototypes to mass‑market products. The next wave of smart glasses, contact‑lens displays, and lightweight head‑mounted units promises to blend the physical world with photorealistic, computer‑generated content in real time. At the heart of this promise lies real‑time video synthesis: the ability to generate or transform video streams on‑device, frame by frame, with latency low enough to feel instantaneous. ...