Generative Augmented Reality: Paradigms, Technologies, and Future Applications
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915629544505344 |
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| author | Liang, Chen Zheng, Jiawen Zeng, Yufeng Tan, Yi Lyu, Hengye Zheng, Yuhui Li, Zisu Weng, Yueting Shi, Jiaxin Zhang, Hanwang |
| author_facet | Liang, Chen Zheng, Jiawen Zeng, Yufeng Tan, Yi Lyu, Hengye Zheng, Yuhui Li, Zisu Weng, Yueting Shi, Jiaxin Zhang, Hanwang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16783 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative Augmented Reality: Paradigms, Technologies, and Future Applications Liang, Chen Zheng, Jiawen Zeng, Yufeng Tan, Yi Lyu, Hengye Zheng, Yuhui Li, Zisu Weng, Yueting Shi, Jiaxin Zhang, Hanwang Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Generative Augmented Reality: Paradigms, Technologies, and Future Applications |
| topic | Human-Computer Interaction Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.16783 |