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Art Museum XR Twin



Development of Space-telling Design Tool and Platform based on Art Museum XR Twin for Supporting Exhibition Planning


Final Research Goal

Our primary objective is to develop an XR Twin-based Space-telling Authoring Tool tailored for art museum curators. This tool aims to empower curators with the capability to virtually curate exhibitions by replicating exhibition spaces and arranging artworks within a virtual environment. To achieve this, our approach encompasses cutting-edge visualization, data transformation, simulation, and visitor information analysis technologies, all meticulously integrated to streamline pre-planning and enable online showcasing. Through this cultural technology research, our ultimate goal is to optimize exhibition curation practices and significantly enhance the overall museum experience.


Focus Areas

  • Artwork Recommendation, Sorting, and Placement Technology based on Artwork Metadata, Exhibition Space Information, Curatorial Narrative, and Visitor Data 
  • Interaction Technology for Simulation in HMD-VR Environment 



We present a virtual exhibition system that automatically optimizes artwork placements in thematic space layouts. Artworks are clustered based on five content factors: color, material, description, artist, and production date. Curators can adjust their importance and cluster sizes according to their design goals. A genetic optimization algorithm is used to determine artwork placement, evaluating spatial characteristics with four cost functions: intra-cluster distance, inter-cluster distance, intra-cluster intervisibility, and occupancy. Additionally, we have developed the “Bubble” element to ensure minimum distances between works based on visitor data, prioritizing safety and comfort. Moreover, curators have the flexibility to fine-tune space arrangements by adjusting default values. We are also developing an interface for collaborative authoring with generative AI to create curatorial narratives, referred to as Space-telling content, suitable for XR twin-based art museum environments. Through the chain method, we aim to simplify content generation and enhance the authoring experience by fostering strong user-AI cooperation.


Expected Contribution

  • Empowering curators with the capability to create virtual exhibitions and conduct simulations in a digital twin environment
  • Facilitating artwork recommendation, placement recommendation, visitor path recommendation, and curatorial narrative creation by leveraging a wide range of contextual elements, including artwork metadata, exhibition space information, curatorial narrative, and visitor data
  • Offering a user-friendly and immersive authoring interaction, ensuring a seamless and convenient exhibition creation experience



This research is supported by the Ministry of Culture, Sports and Tourism and Korea Creative Content Agency(Project Number: R2021080001)