Holo-Artisan: A Personalized Multi-User Holographic Experience for Virtual Museums on the Edge Intelligence

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 personalization, we integrate federated learning (FL) -- edge devices locally fine-tune AI models and share only model updates for aggregation. This edge-centric approach minimizes latency and bandwidth usage, ensuring a synchronized shared experience with individual customization. Through Holo-Artisan, static museum exhibits are transformed into dynamic, living artworks that engage each visitor in a personal dialogue, heralding a new paradigm of cultural heritage interaction.