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

arXiv: https://arxiv.org/abs/2408.14644 · HTML: https://mlsanity.com/paper/2408.14644/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets](https://mlsanity.com/paper/2202.00273/) (2022)
- [FILM: Frame Interpolation for Large Motion](https://mlsanity.com/paper/2202.04901/) (2022)
- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [Understanding and Creating Art with AI: Review and Outlook](https://mlsanity.com/paper/2102.09109/) (2021)
- [Public Perception of Generative AI on Twitter: An Empirical Study Based on Occupation and Usage](https://mlsanity.com/paper/2305.09537/) (2023)
- [Learning to See: You Are What You See](https://mlsanity.com/paper/2003.00902/) (2020)
- [Synthetic Books](https://mlsanity.com/paper/2201.09518/) (2022)
- [Visions Of Destruction: Exploring Human Impact on Nature by Navigating the Latent Space of a Diffusion Model via Gaze](https://mlsanity.com/paper/2401.06361/) (2023)
- [Explaining CLIP through Co-Creative Drawings and Interaction](https://mlsanity.com/paper/2306.07429/) (2023)
- [Artistic Strategies to Guide Neural Networks](https://mlsanity.com/paper/2307.07521/) (2023)

## Built on by (most cited first, 1 of 1)

- [Quantum est in Libris: Navigating Archives with GenAI, Uncovering Tension Between Preservation and Innovation](https://mlsanity.com/paper/2509.25237/) (2025)

## 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.