用生成模型统一增强现实，实现更逼真沉浸的虚实融合体验
Generative Augmented Reality: Paradigms, Technologies, and Future Applications
- 以统一生成模型替代传统AR多模块流程，联合编码环境与交互信号
- 支持连续视频生成，实现高保真、强互动的实时虚实融合
- 适合研究生成式AI与空间计算交叉领域，推动下一代AR发展
本文提出生成式增强现实（Generative Augmented Reality, GAR），作为一种下一代范式，将增强现实重新定义为世界重合成而非传统AR引擎的组合过程。GAR用统一的生成骨干网络取代传统AR引擎的多阶段模块，将环境感知、虚拟内容和交互信号联合编码为连续视频生成的条件输入。论文形式化了AR与GAR之间的计算对应关系，综述了实现实时生成增强现实的技术基础，并展望了其潜在应用。GAR有望在真实感、交互性和沉浸感方面带来高质量体验，同时引发关于技术、内容生态以及伦理社会影响的新研究挑战。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。