A Proposed XR-based Digital Framework for Nakhwa-nori: Preserving Regional Traditional Festivals and Addressing Safety and Environmental Barrier

Hatnim Kim, Geonwoo Song, Seungryeol Eom, Seungkwan Choi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 4457-4464

Abstract


Nakhwa-nori, a traditional Korean fireworks display with over 500 years of history, now faces preservation challenges due to growing public interest that has led to safety risks from large crowds and limited accessibility. This digital reconstruction aims to preserve cultural identity and ensure that this unique ritual is not lost or misrepresented. This paper proposes and develops a prototype real-time 3D reconstruction system for traditional fireworks using Unreal Engine, demonstrating the feasibility of XR-based cultural preservation approaches. The dual-layer particle system simulates large-scale environmental effects and individual ember trajectories with physics-based wind simulation. The prototype was built using key Unreal Engine features such as Nanite virtualized geometry, Lumen global illumination for night lighting, and GPU-accelerated Niagara particle systems. Spline-based rope systems and fire-on-water effects are planned for future implementation. Performance evaluation on consumer hardware demonstrated the potential to reproduce key visual elements such as particle density, lighting conditions, and spatial relationships. This study suggests practical applications of real-time rendering technologies for cultural heritage documentation and indicates that the approach could be extended to other traditions requiring complex particle effects.

Related Material


[pdf]
[bibtex]
@InProceedings{Kim_2025_ICCV, author = {Kim, Hatnim and Song, Geonwoo and Eom, Seungryeol and Choi, Seungkwan}, title = {A Proposed XR-based Digital Framework for Nakhwa-nori: Preserving Regional Traditional Festivals and Addressing Safety and Environmental Barrier}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2025}, pages = {4457-4464} }