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

Fuente: arXiv
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Autori principali: Kuo, Nan-Hong, Baghban, Hojjat
Natura: Preprint
Pubblicazione: 2025
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author Kuo, Nan-Hong
Baghban, Hojjat
author_facet Kuo, Nan-Hong
Baghban, Hojjat
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2508_14956
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Holo-Artisan: A Personalized Multi-User Holographic Experience for Virtual Museums on the Edge Intelligence
Kuo, Nan-Hong
Baghban, Hojjat
Multimedia
Networking and Internet Architecture
Systems and Control
Image and Video Processing
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.
title Holo-Artisan: A Personalized Multi-User Holographic Experience for Virtual Museums on the Edge Intelligence
topic Multimedia
Networking and Internet Architecture
Systems and Control
Image and Video Processing
url https://arxiv.org/abs/2508.14956