AI RESEARCH
SEGAR: Selective Enhancement for Generative Augmented Reality
arXiv CS.CV
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ArXi:2603.24541v1 Announce Type: new Generative world models offer a compelling foundation for augmented-reality (AR) applications: by predicting future image sequences that incorporate deliberate visual edits, they enable temporally coherent, augmented future frames that can be computed ahead of time and cached, avoiding per-frame rendering from scratch in real time. In this work, we present SEGAR, a preliminary framework that combines a diffusion-based world model with a selective correction stage to this vision.