Raiders of the Lost Art
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Abstract (arXiv)
Neural style transfer, first proposed by Gatys et al. (2015), can be used to create novel artistic work through rendering a content image in the form of a style image. We present a novel method of reconstructing lost artwork, by applying neural style transfer to x-radiographs of artwork with secondary interior artwork beneath a primary exterior, so as to reconstruct lost artwork. Finally we reflect on AI art exhibitions and discuss the social, cultural, ethical, and philosophical impact of these technical innovations.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Sep 2019.
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Citing papers per year: 2021: 3.
3 papers.
- 2021Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholarscited by 2
- 2021Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networkscited by 1
- 2021Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motioncited by 1
- 2021Hierarchical Graph-Convolutional Variational AutoEncoding for Generative Modelling of Human Motion24 Nov 2021
- 2021Resolution enhancement in the recovery of underdrawings via style transfer by generative adversarial deep neural networks30 Jan 2021
- 2021Recovery of underdrawings and ghost-paintings via style transfer by deep convolutional neural networks: A digital tool for art scholars4 Jan 2021