Neuromorphic Data Transformations for Sustainable VR Art Applications

Anna Shvets () and Anthony Trzepizur ()
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Anna Shvets: University of Nottingham, School of Computer Science
Anthony Trzepizur: Pôle Digital iMSA

A chapter in XR and Metaverse, 2026, pp 169-181 from Springer

Abstract: Abstract A previously proposed method for adapting 2D digital audio-visual artwork to virtual reality (VR) environments utilized time-distributed data (TDD) generators derived from the neuromorphic computing domain. This research advances that approach by emphasizing sustainability, demonstrating improved VR performance and reduced disk space requirements for frames processed through the TDD pipeline compared to raw data. Additionally, we introduce an improved colour palette preservation technique using a mask filtering step, where neuromorphic data serve as a filter for initial colour values. Furthermore, we show that neuromorphic data transformation effectively eliminates noise, providing a viable solution for addressing outliers. The pipeline code and received metrics results are available online: https://github.com/asnota/TDD-method

Keywords: Time-distributed data; Neuromorphic computing; VR; Music XR (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-11983-4_13

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DOI: 10.1007/978-3-032-11983-4_13

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