Compressing a Lifetime: Exploiting Long-Horizon Temporal Redundancy for Persistent Visual Memory

Persistent visual memory is essential for always-on smart-glass assistants, but its storage cost is prohibitive: recording 2K video at 10 frames per second produces roughly 17 PB of raw RGB data over a 25-year lifetime, far beyond practical storage limits. Existing systems often reduce storage by selecting salient observations, irreversibly discarding visual evidence that may matter for unforeseen future queries. We instead formulate persistent visual memory as a memory-compression problem: retaining a compact representation of the complete observation stream rather than replacing it with selected frames or textual summaries. Our key insight is that egocentric video contains substantial long-horizon redundancy, with similar observations recurring across days and months, yet existing video codecs mainly exploit spatial and short-range temporal redundancy. We introduce a system that leverages this recurring context during both storage and reconstruction. A multi-rate neural video codec supports a wide range of compression levels, a novelty-aware rate selector assigns different rates to observations, and a memory-guided generative decoder reconstructs highly compressed content. Across three egocentric benchmarks, our system achieves compression ratios closed to $10000\times$, beyond the practical range of existing baselines, while substantially improving reconstruction quality and downstream video understanding under extreme compression.