Generative Augmented Reality: Paradigms, Technologies, and Future Applications

• URL: http://arxiv.org/abs/2511.16783v1
• Date: Thu, 20 Nov 2025 20:17:14 GMT
• Title: Generative Augmented Reality: Paradigms, Technologies, and Future Applications
• Authors: Chen Liang, Jiawen Zheng, Yufeng Zeng, Yi Tan, Hengye Lyu, Yuhui Zheng, Zisu Li, Yueting Weng, Jiaxin Shi, Hanwang Zhang,
• Abstract summary: Generative Augmented Reality (GAR) is a next-generation paradigm that reframes augmentation as a process of world re-synthesis.<n>GAR replaces the conventional AR engine's multi-stage modules with a unified generative backbone.<n>We envision GAR as a future AR paradigm that delivers high-fidelity experiences in terms of realism, interactivity, and immersion.
• Score: 63.44593851261096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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