mlsanityData to 7 Oct 2026

Visions of Destruction: Exploring a Potential of Generative AI in Interactive Art

arXiv 2408.14644 ↗First posted 26 Aug 2024cs.HC cs.AIcited by 1

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Abstract (arXiv)

This paper explores the potential of generative AI within interactive art, employing a practice-based research approach. It presents the interactive artwork "Visions of Destruction" as a detailed case study, highlighting its innovative use of generative AI to create a dynamic, audience-responsive experience. This artwork applies gaze-based interaction to dynamically alter digital landscapes, symbolizing the impact of human activities on the environment by generating contemporary collages created with AI, trained on data about human damage to nature, and guided by audience interaction. The transformation of pristine natural scenes into human-made and industrialized landscapes through viewer interaction serves as a stark reminder of environmental degradation. The paper thoroughly explores the technical challenges and artistic innovations involved in creating such an interactive art installation, emphasizing the potential of generative AI to revolutionize artistic expression, audience engagement, and especially the opportunities for the interactive art field. It offers insights into the conceptual framework behind the artwork, aiming to evoke a deeper understanding and reflection on the Anthropocene era and human-induced climate change. This study contributes significantly to the field of creative AI and interactive art, blending technology and environmental consciousness in a compelling, thought-provoking manner.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 43 references that are in the map. First posted on arXiv 26 Aug 2024.

Builds on

14 of 43 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
  3. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  4. 2016Generative Adversarial Text to Image Synthesiscited by 735
  5. 2022StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasetscited by 489
  6. 2022FILM: Frame Interpolation for Large Motioncited by 149
  7. 2023Art and the science of generative AI: A deeper divecited by 93
  8. 2021Understanding and Creating Art with AI: Review and Outlookcited by 60
  9. 2023Public Perception of Generative AI on Twitter: An Empirical Study Based on Occupation and Usagecited by 14
  10. 2020Learning to See: You Are What You Seecited by 4
  11. 2022Synthetic Bookscited by 3
  12. 2023Visions Of Destruction: Exploring Human Impact on Nature by Navigating the Latent Space of a Diffusion Model via Gazecited by 3
2 more
  1. 2023Explaining CLIP through Co-Creative Drawings and Interactioncited by 2
  2. 2023Artistic Strategies to Guide Neural Networkscited by 2

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