XR Blocks: Accelerating Human-centered AI + XR Innovation
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908567083155456 |
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| author | Li, David Numan, Nels Qian, Xun Chen, Yanhe Zhou, Zhongyi Alekseev, Evgenii Lee, Geonsun Cooper, Alex Xia, Min Chung, Scott Nelson, Jeremy Yuan, Xiuxiu Dias, Jolica Bettridge, Tim Hersh, Benjamin Huynh, Michelle Piascik, Konrad Cabello, Ricardo Kim, David Du, Ruofei |
| author_facet | Li, David Numan, Nels Qian, Xun Chen, Yanhe Zhou, Zhongyi Alekseev, Evgenii Lee, Geonsun Cooper, Alex Xia, Min Chung, Scott Nelson, Jeremy Yuan, Xiuxiu Dias, Jolica Bettridge, Tim Hersh, Benjamin Huynh, Michelle Piascik, Konrad Cabello, Ricardo Kim, David Du, Ruofei |
| contents | We are on the cusp where Artificial Intelligence (AI) and Extended Reality (XR) are converging to unlock new paradigms of interactive computing. However, a significant gap exists between the ecosystems of these two fields: while AI research and development is accelerated by mature frameworks like JAX and benchmarks like LMArena, prototyping novel AI-driven XR interactions remains a high-friction process, often requiring practitioners to manually integrate disparate, low-level systems for perception, rendering, and interaction. To bridge this gap, we present XR Blocks, a cross-platform framework designed to accelerate human-centered AI + XR innovation. XR Blocks strives to provide a modular architecture with plug-and-play components for core abstraction in AI + XR: user, world, peers; interface, context, and agents. Crucially, it is designed with the mission of "reducing frictions from idea to reality", thus accelerating rapid prototyping of AI + XR apps. Built upon accessible technologies (WebXR, three.js, TensorFlow, Gemini), our toolkit lowers the barrier to entry for XR creators. We demonstrate its utility through a set of open-source templates, samples, and advanced demos, empowering the community to quickly move from concept to interactive XR prototype. Site: https://xrblocks.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25504 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | XR Blocks: Accelerating Human-centered AI + XR Innovation Li, David Numan, Nels Qian, Xun Chen, Yanhe Zhou, Zhongyi Alekseev, Evgenii Lee, Geonsun Cooper, Alex Xia, Min Chung, Scott Nelson, Jeremy Yuan, Xiuxiu Dias, Jolica Bettridge, Tim Hersh, Benjamin Huynh, Michelle Piascik, Konrad Cabello, Ricardo Kim, David Du, Ruofei Human-Computer Interaction Artificial Intelligence Graphics Software Engineering H.5.1; D.2.2; H.5.m; D.2.m We are on the cusp where Artificial Intelligence (AI) and Extended Reality (XR) are converging to unlock new paradigms of interactive computing. However, a significant gap exists between the ecosystems of these two fields: while AI research and development is accelerated by mature frameworks like JAX and benchmarks like LMArena, prototyping novel AI-driven XR interactions remains a high-friction process, often requiring practitioners to manually integrate disparate, low-level systems for perception, rendering, and interaction. To bridge this gap, we present XR Blocks, a cross-platform framework designed to accelerate human-centered AI + XR innovation. XR Blocks strives to provide a modular architecture with plug-and-play components for core abstraction in AI + XR: user, world, peers; interface, context, and agents. Crucially, it is designed with the mission of "reducing frictions from idea to reality", thus accelerating rapid prototyping of AI + XR apps. Built upon accessible technologies (WebXR, three.js, TensorFlow, Gemini), our toolkit lowers the barrier to entry for XR creators. We demonstrate its utility through a set of open-source templates, samples, and advanced demos, empowering the community to quickly move from concept to interactive XR prototype. Site: https://xrblocks.github.io |
| title | XR Blocks: Accelerating Human-centered AI + XR Innovation |
| topic | Human-Computer Interaction Artificial Intelligence Graphics Software Engineering H.5.1; D.2.2; H.5.m; D.2.m |
| url | https://arxiv.org/abs/2509.25504 |