Year: 2025
Type: preprint
Abstract: 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
Source: arXiv (Cornell University)
Authors: 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
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7084074983