Generative Augmented Reality: Paradigms, Technologies, and Future Applications

TL;DR

This paper proposes Generative Augmented Reality (GAR), a new paradigm that uses a unified generative model for real-time, high-fidelity AR experiences, replacing traditional multi-stage AR systems.

Contribution

It formalizes the concept of GAR, surveys its technical foundations, and outlines future applications and research challenges in this emerging field.

Findings

GAR enables real-time, high-fidelity AR experiences.

Unified generative backbone simplifies AR system architecture.

Potential applications include immersive, realistic AR environments.

Abstract

This paper introduces Generative Augmented Reality (GAR) as a next-generation paradigm that reframes augmentation as a process of world re-synthesis rather than world composition by a conventional AR engine. GAR replaces the conventional AR engine's multi-stage modules with a unified generative backbone, where environmental sensing, virtual content, and interaction signals are jointly encoded as conditioning inputs for continuous video generation. We formalize the computational correspondence between AR and GAR, survey the technical foundations that make real-time generative augmentation feasible, and outline prospective applications that leverage its unified inference model. We envision GAR as a future AR paradigm that delivers high-fidelity experiences in terms of realism, interactivity, and immersion, while eliciting new research challenges on technologies, content ecosystems, and the ethical and societal implications.