AccScience Publishing / AC / Volume 2 / Issue 2 / DOI: 10.36922/ac.2719
ARTICLE

Simulacra and historical fidelity in digital recreation of lost cultural heritage: Reconstituting period materialities for the period eye

Trent Olsen1* James Hutson2 Charles O'Brien1 Jeremiah Ratican3
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1 Department of Art History and Visual Culture, Lindenwood University, Professor of Art History and Visual Culture, Saint Charles, Missouri, United States of America
2 Department of Art, Art History, and Design, University of Alabama Huntsville, Assistant Professor of Art, Huntsville, Alabama, United States of America
3 Department of Art, Media and Production, Associate Professor of Game Design, Lindenwood University, Saint Charles, Missouri, United States of America
Submitted: 12 January 2024 | Accepted: 14 March 2024 | Published: 14 May 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

The advancement of digital technologies in art history has opened avenues for reconstructing lost or damaged cultural heritage, a need highlighted by the deteriorated state of many artworks from the 1785 Salon. Grounded in the concept of the “Period Eye” by art historian Michael Baxandall, which emphasizes understanding artworks within their original historical and cultural contexts, this study proposes a subfield focused on Reconstituting Period Materialities for the Period Eye. This methodology bridges comprehensive historical research with generative visual artificial intelligence (AI) technologies, facilitating the creation and immersive virtual reality viewing of artworks. Beyond mere visual replication, the approach aims to recreate the material and textural realities of the period, thereby enabling contemporary audiences to experience these works as they were originally perceived. The process includes replicating building materials using Quixel Megascans, employing AI for generating images of lost artworks, and utilizing normal maps for simulating painting textures, all contributing to an authentic reconstruction of the Salon’s ambiance and materiality. This approach, met with some skepticism from traditional historians and archeologists, asserts that such digital reconstitution, backed by rigorous empirical research and detailed period-specific datasets, yields reconstructions of greater historical accuracy and contextual richness. This mirrors strides in sound archeology, endorsing a similar empirical approach in visual material recreation. The significance of this study is underscored by its potential to enrich our comprehension of historical artworks through a “Period Eye,” blending historical insights with modern technological innovation for a deeper understanding and appreciation of cultural heritage.

Keywords
Digital reconstruction
Cultural heritage
Generative visual artificial intelligence
Period materialities
Period Eye
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
References
  1. Arsa G, de Jesus Lima LC, Motta-Santos D, et al. Effects of prior exercise on glycemic responses following carbohydrate inges on in individuals with type 2 diabetes. J Clin Transl Res. 2015;1(1):22-30. doi: 10.18053/jctres.201501.002

 

  1. Blanco EE, Meade JC, Richards WD, Ophthalmic V. Surgical Stapling System. US Patent. 4,969,591; 1990.

 

  1. Conway KM. Critical quantitative study of immigrant students. In: Stage FK, Wells RS, editors. New Scholarship in Critical Quantitative Research - Part 1. San Francisco: Jossey-Bass; 2014. p. 51-64.

 

  1. Este J, Warren C, Connor L. Life in the Clickstream: The Future of Journalism. Media Entertainment and Arts Alliance; 2008. Available from: https://www.alliance.org.au/documents/ foj_report_final.pdf [Last accessed on 2021 Jan 05].

 

  1. Gale L. The Relationship between Leadership and Employee Empowerment for Successful Total Quality Management. [PhD Thesis, University of Western Sydney]. Australasian Digital Thesis Database; 2000.

 

  1. Liu ZS. Zhongmei maoyizhan dui Zhongguo jingji fazhan yu yingdui qihou bianhua de yingxiang ji yingdui [The influence of the trade war between China and the United States on China’s economic development and its response to climate change and relevant countermeasures]. Shijie Huan. 2020(1):43-45. doi: 10.1590/shijiehuanjing-4989201100108 [Article in Chinese]

 

  1. Moreno C, Cendales R. Mortalidad y años potenciales de vida perdidos por homicidios en Colombia, 1985-2006 [Mortality and potential loss of life caused by murders in Colombia from 1985 to 2006]. Rev Panam Salud Pública. 2011;30(4):342-353. doi: 10.1590/S1020-4989201100108 [Article in Spanish]

 

  1. National Commission of Audit. Report to the Commonwealth Government. Canberra: Australian Government Publishing Service; 1996.

 

  1. Obisesan TO, Gillum RF. Cognitive function, social integration and mortality in a U.S. National cohort study of older adults. BMC Geriatr. 2009;9(2):33. doi: 10.1186/1471-2318-9-33

 

  1. Roberts S. Early String Ties us to Neanderthals. The New York Times; 2020. Available from: https://www.nytimes. com/2020/04/09/science/neanderthals-fiber-string-math. html [Last accessed on 2021 Jan 05].

 

  1. Schneider Z, Whitehead D, Elliott D. Nursing and Midwifery Research: Methods and Appraisal for Evidence-based Practice. 3rd ed. Marrickville, NSW: Elsevier Australia; 2007.

 

  1. Standards Australia. Glass in Buildings: Selection and Installation, AS 1288-2006; 2006. https://www.sai global database [Last accessed on 2008 Jan 31].

 

  1. Wiskunde B, Arslan M, Fischer P, et al. Indie pop rocks mathematics: Twenty One Pilots, Nicolas Bourbaki, and the empty set. J Improbable Mathe. 2019;27(1):1935-1968. doi: 10.0000/3mp7y-537

 

  1. Ulgen A, Gürkut O, Li W. Potential predictive factors for breast cancer subtypes from a North cyprus cohort analysis. Cyprus J Med Sci. 2020;5:339-349. doi: 10.5152/cjms.2020.2291

 

  1. United Nations. World Population Prospects: The 2017 Revision. Key Findings and Advance Tables. New York, USA: United Nations Publications; 2017. Available from: https://esa.un.org/unpd/wpp/publications/files/wpp2017_ keyfindings.pdf [Last accessed on 2021 Jan 05].

 

  1. World Health Organization. Comprehensive Implementation Plan on Maternal, Infant and Young Child Nutrition; 2014. Available from: https://apps.who.int/iris/bitstream/ handle/10665/113048/who_nmh_nhd_14.1_eng.pdf?ua=1 [Last accessed on 2021 Jan 05].

 

  1. Rua H, Alvito P. Living the past: 3D models, virtual reality and game engines as tools for supporting archaeology and the reconstruction of cultural heritage-the case-study of the Roman villa of Casal de Freiria. J Archaeol Sci. 2011;38(12):3296-3308. doi: 10.1016/j.jas.2011.07.015

 

  1. Foster GM. The Limits of the Lost Cause: Essays on Civil War Memory. Baton Rouge: LSU Press; 2024.

 

  1. Maier CS. Consigning the twentieth century to history: Alternative narratives for the modern era. Am Hist Rev. 2000;105(3):807-831. doi: 10.2307/2651811

 

  1. Allison D, Fredrickson L, Gardner SA, et al. Media and Repository Support Unit, University of Nebraska-Lincoln Libraries, Annual Report July 2018-June 2019; 2019.

 

  1. Polymenopoulou E. Rembrandt’s missing piece: AI art and the fallacies of copyright law. Washington J Law Technol Arts Forthcoming. 2024;19(4):64-88. doi: 10.2139/ssrn.4794932

 

  1. Kaplan F. Big Data of the past, from Venice to Europe. In: Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems; 2020. p. 1.

 

  1. Gaber JA, Youssef SM, Fathalla KM. The role of artificial intelligence and machine learning in preserving cultural heritage and art works via virtual restoration. ISPRS Ann Photogrammetry Remote Sens Spat Inform Sci. 2023;X-1/ W1-2023:185-190. doi: 10.5194/isprs-annals-X-1-W1-2023-185-2023

 

  1. Stahl BC. Artificial Intelligence for a Better Future: An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies. Berlin: Springer Nature; 2021. p. 124.

 

  1. Rodin S. Time, history and legal interpretation. Maastricht J Eur Comp Law. 2021;28(4):433-436. doi: 10.1177/1023263X211039980

 

  1. Potter MC, editor. Representing the Past in the Art of the Long Nineteenth Century: Historicism, Postmodernism, and Internationalism. Milton Park: Routledge; 2021.

 

  1. Currie A. Stepping forwards by looking back: Underdetermination, epistemic scarcity and legacy data. Perspect Sci. 2021;29(1):104-132. doi: 10.1162/posc_a_00362

 

  1. Swaim DG. Time’s Deep Rhythms: Models, Mechanisms, and Narratives in Historical Explanation. (Doctoral Dissertation, University of Pennsylvania); 2022.

 

  1. Bateman DA, Teele DL. A developmental approach to historical causal inference. Public Choice. 2020;185(3):253-279. doi: 10.1007/s11127-019-00713-4

 

  1. Yu T, Lin C, Zhang S, et al. Artificial intelligence for Dunhuang cultural heritage protection: The project and the dataset. Int J Comput Vis. 2022;130(11):2646-2673. doi: 10.1007/s11263-022-01665-x

 

  1. Cameron FR. The Future of Digital Data, Heritage and Curation: In a More-Than-Human World. Milton Park: Routledge; 2021.

 

  1. Hutson J, Hutson P. Immersive technologies. In: Inclusive Smart Museums: Engaging Neurodiverse Audiences and Enhancing Cultural Heritage. Cham: Springer Nature Switzerland; 2024. p. 153-228.

 

  1. Roussou M. Learning by doing and learning through play: An exploration of interactivity in virtual environments for children. Comput Entertain. 2004;2(1):10.

 

  1. Santos P, Ritz M, Fuhrmann C, et al. Acceleration of 3D mass digitization processes: Recent advances and challenges. In: Mixed Reality and Gamification for Cultural Heritage. Cham: Springer; 2017. p. 99-128. doi: 10.1007/978-3-319-49607-8_4

 

  1. Terras, M. Cultural heritage information: Artefacts and digitization technologies. In: Cultural Heritage Information: Access and Management. London: Facet; 2015. p. 63-88. doi: 10.29085/9781783300662.005

 

  1. Zhou M, Geng G, Wu Z. Digital Preservation Technology for Cultural Heritage. Beijing: Higher Education Press; 2012. doi: 10.1007/978-3-642-28099-3

 

  1. Loscos C, Tecchia F, Frisoli A, et al. The Museum of Pure Form: Touching Real Statues in an Immersive Virtual Museum. In VAST: The 5th International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage; 2004. p. 271-279. doi: 10.2312/VAST/VAST04/271-279

 

  1. Bevan B, Dillon J. Broadening views of learning: Developing educators for the 21st century through an international research partnership at the Exploratorium and King’s College London. New Educ. 2010;6(3-4):167-180. doi: 10.1080/1547688X.2010.10399599

 

  1. Gomez GA, Levine S. Grant, Ralph Wanger, and the Center for the Art of East Asia, the University of Chicago. Voices. 2021;773:1060.

 

  1. Pesce D, Neirotti P, Paolucci E. When culture meets digital platforms: Value creation and stakeholders’ alignment in big data use. Curr Issues Tour. 2019;22(15):1883-1903. doi: 10.1080/13683500.2019.1591354

 

  1. Sandheinrich P, Hutson J. Haptic preservation of cultural ephemera: An extended reality solution using stereoscopic experience replication for victorian parlor culture. Metaverse Basic Appl Res. 2023;2:48. doi: 10.56294/mr202348

 

  1. Bevilacqua MG, Russo M, Giordano A, Spallone R. 3D Reconstruction, Digital Twinning, and Virtual Reality: Architectural Heritage Applications. In: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). Piscataway: IEEE; 2022. p. 92-96.

 

  1. Tan J, Leng J, Zeng X, Feng D, Yu P. Digital twin for Xiegong’s architectural archaeological research: A case study of Xuanluo Hall, Sichuan, China. Buildings. 2022;12(7):1053. doi: 10.3390/buildings12071053

 

  1. Kim J, Lee BK. A research on the possibility of restoring cultural assets of artificial intelligence through the application of artificial neural networks to roof tile (Wadang). J Korea Soc Comput Inf. 2021;26(1):19-26.

 

  1. Li J. Application of artificial intelligence in cultural heritage protection. J Phys Conf Ser. 2021;1881(3):032007. doi: 10.1088/1742-6596/1881/3/032007

 

  1. D’Orazio M, Bernardini G, Di Giuseppe E. Predict the priority of end-users’ maintenance requests and the required technical staff through LSTM and Bi-LSTM recurrent neural networks. Facilities. 2023;41(15/16):38-51. doi: 10.1108/F-07-2022-0093

 

  1. Moreno M, Prieto AJ, Ortiz R, et al. Preventive conservation and restoration monitoring of heritage buildings based on fuzzy logic. Int J Archit Herit. 2023;17(7):1153-1170. doi: 10.1080/15583058.2021.2018520

 

  1. Bordoni L, Ardissono L, Barceló JA, et al. The contribution of AI to enhance understanding of Cultural Heritage. Intell Artif. 2013;7(2):101-112. doi: 10.3233/IA-130052

 

  1. Ranaldi L, Fallucchi F, Zanzotto FM. Dis-cover ai minds to preserve human knowledge. Future Internet. 2021;14(1):10. doi: 10.3390/fi14010010

 

  1. Girard G, Rafael-Patiño J, Truffet R, et al. Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge. NeuroImage. 2023;277:120231. doi: 10.1016/j.neuroimage.2023.120231

 

  1. Howes A. Arts and Minds: How the Royal Society of Arts Changed a Nation. Princeton: Princeton University Press; 2020.

 

  1. Bowman M. A statistical analysis of the catalogues and criticism of the 19th-century Paris Fine Art Salon: The emergence of titling in the French art world. Digit Scholarsh Humanit. 2023;38(3):978-996. doi: 10.1093/llc/fqac088

 

  1. Zampronha E. Interdisciplinarity as a basis for the artistic and musical creation: An example connecting archeology and music in a visual-sound installation. In: CIVAE 2021. 3rd ed. Madrid, España: MusicoGuia; 2021. p. 189-193.

 

  1. Till R. Sound archaeology: A study of the acoustics of three world heritage sites, Spanish prehistoric painted caves, Stonehenge, and paphos theatre. Acoustics. 2019;1(3):661-692. doi: 10.3390/acoustics1030039

 

  1. Debertolis P, Gullà D. New technologies of analysis in archaeoacoustics. In: Archaeoacoustics II, The Archaeology of Sound, Publication of the 2015 Conference in Istanbul. Vol. 2. The OTS Foundation; 2016. p. 33-50.

 

  1. Neuhoff J. Ecological Psychoacoustics. Leiden: Brill; 2021.

 

  1. Banari N. Applications of Artificial Intelligence for the Resource-scarce Cultural Heritage Domain: From Language and Image Processing to Multi-modality (Doctoral Dissertation, University of Antwerp); 2022.

 

  1. Lang S, Ommer B. Transforming Information Into Knowledge: How Computational Methods Reshape Art History. Digit Humanit Q. 2021;15(3).

 

  1. Galanos V. Expectations and Expertise in Artificial Intelligence: Specialist Views and Historical Perspectives on Conceptualisation, Promise, and Funding. Doctoral Thesis, The University of Edinburgh. doi: 10.7488/era/3188 2023

 

  1. Langdale A. Aspects of the critical reception and intellectual history of Baxandall’s concept of the period eye. Art Hist. 1998;21(4):479-497. doi: 10.1111/1467-8365.00126

 

  1. Whyte R. Exhibitions of manuscript verse in the salon du louvre. In: Studies in Eighteenth-Century Culture. Vol. 48. Baltimore: Johns Hopkins University Press; 2019. p. 57-73. doi: 10.1353/sec.2019.0005

 

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Arts & Communication, Electronic ISSN: 2972-4090 Published by AccScience Publishing