{"ID":2880335,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14956","arxiv_id":"2508.14956","title":"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\\\u0026A 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.","short_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 vi...","url_abs":"https://arxiv.org/abs/2508.14956","url_pdf":"https://arxiv.org/pdf/2508.14956v1","authors":"[\"Nan-Hong Kuo\",\"Hojjat Baghban\"]","published":"2025-08-20T16:32:23Z","proceeding":"cs.MM","tasks":"[\"cs.MM\",\"cs.NI\",\"eess.IV\",\"eess.SY\"]","methods":"[]","has_code":false}