Holo-Artisan: A Personalized Multi-User Holographic Experience for Virtual Museums on the Edge Intelligence
Nan-Hong Kuo, Hojjat Baghban

TL;DR
Holo-Artisan introduces a system for personalized, multi-user holographic virtual museum experiences using edge computing, generative AI, and federated learning to create dynamic, interactive digital artworks tailored to each visitor.
Contribution
The paper presents a novel architecture combining true holographic displays, real-time multi-user personalization, and federated learning for privacy-preserving AI model updates in virtual museums.
Findings
Real-time personalized holographic interactions achieved
Edge computing reduces latency and bandwidth
Dynamic digital artworks enhance visitor engagement
Abstract
We present Holo-Artisan, a novel system architecture enabling immersive multi-user experiences in virtual museums through true holographic displays and personalized edge intelligence. In our design, local edge computing nodes process real-time user data -- including pose, facial expression, and voice -- for multiple visitors concurrently. Generative AI models then drive digital artworks (e.g., a volumetric Mona Lisa) to respond uniquely to each viewer. For instance, the Mona Lisa can return a smile to one visitor while engaging in a spoken Q\&A with another, all in real time. A cloud-assisted collaboration platform composes these interactions in a shared scene using a universal scene description, and employs ray tracing to render high-fidelity, personalized views with a direct pipeline to glasses-free holographic displays. To preserve user privacy and continuously improve…
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Taxonomy
TopicsAugmented Reality Applications · Virtual Reality Applications and Impacts · Interactive and Immersive Displays
