Exploring Aesthetic Qualities of Deep Generative Models through Technological (Art) Mediation

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Abstract

Deep Generative Models (DGM) have had a great impact both on visual art and broader visual culture. In this research-through-design project we investigate the use of a DGM for helping museum visitors explore the aesthetics of Edvard Munch’s art. We designed and built an interactive drawing table that allows a user to explore a StyleGAN model trained on sketches by Edvard Munch. The paper makes two novel contributions: 1. It presents a system that allows users to interact with a DGM by drawing on paper (rather than the typical text prompts used by most current systems). 2. We demonstrate how this mode and quality of interaction establish a unique perspective on Munch’s drawings as a practice. Through qualitative evaluation, we discuss how this setup led users towards a specific hermeneutic drawing strategy that enables building competency with the model and by proxy the data it is trained on. We suggest that the resulting interaction may contribute to an "education of attention" helping museum visitors to become attentive to certain visual qualities in Munch’s drawing practice. Finally, we discuss how the concepts of technological mediation and relationality are useful for designing how the output of a DGM is understood by its users.
OriginalsprogEngelsk
TitelProceedings of the 2024 ACM Designing Interactive Systems Conference
Antal sider15
UdgivelsesstedNew York, NY, USA
ForlagAssociation for Computing Machinery
Publikationsdato1 jun. 2024
Sider2738-2752
ISBN (Elektronisk)9798400705830
DOI
StatusUdgivet - 1 jun. 2024
BegivenhedDesigning Interactive Systems 2024: Why Design? - IT University of Copenhagen, Copenhagen, Danmark
Varighed: 1 jul. 20245 jul. 2024
https://dis.acm.org/2024/

Konference

KonferenceDesigning Interactive Systems 2024
LokationIT University of Copenhagen
Land/OmrådeDanmark
ByCopenhagen
Periode01/07/202405/07/2024
Internetadresse

Emneord

  • aesthetics
  • Deep generative models
  • drawing
  • fine art
  • interaction design
  • machine learning
  • postphenomenology
  • StyleGAN

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