mlsanityData to 13 Oct 2026

"An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals

arXiv 2302.12601 ↗First posted 24 Feb 2023cs.HC cs.AIcited by 11

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Abstract (arXiv)

Text-to-image generation (TTIG) models, a recent addition to creative AI, can generate images based on a text description. These models have begun to rival the work of professional creatives, and sparked discussions on the future of creative work, loss of jobs, and copyright issues, amongst other important implications. To support the sustainable adoption of TTIG, we must provide rich, reliable and transparent insights into how professionals perceive, adopt and use TTIG. Crucially though, the public debate is shallow, narrow and lacking transparency, while academic work has focused on studying the use of TTIG in a general artist population, but not on the perceptions and attitudes of professionals in a specific industry. In this paper, we contribute a qualitative, exploratory interview study on TTIG in the Finnish videogame industry. Through a Template Analysis on semi-structured interviews with 14 game professionals, we reveal 12 overarching themes, structured into 49 sub-themes on professionals' perception, adoption and use of TTIG systems in games industry practice. Experiencing (yet another) change of roles and creative processes, our participants' reflections can inform discussions within the industry, be used by policymakers to inform urgently needed legislation, and support researchers in games, HCI and AI to support the sustainable, professional use of TTIG to benefit people and games as cultural artefacts.

Key facts. Cited by 11 papers in the map (data through 13 Oct 2026). Builds on 12 of its 102 references that are in the map. First posted on arXiv 24 Feb 2023.

Builds on

12 of 102 references.

References · 12

  1. 2017Attention Is All You Needcited by 60,106
  2. 2020Language Models are Few-Shot Learnerscited by 32,320
  3. 2020Denoising Diffusion Probabilistic Modelscited by 20,952
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,777
  5. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,564
  6. 2021Zero-Shot Text-to-Image Generationcited by 4,284
  7. 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 138
  8. 2015Generating Images from Captions with Attentioncited by 136
  9. 2022The Creativity of Text-to-Image Generationcited by 73
  10. 2022Large-scale Text-to-Image Generation Models for Visual Artists' Creative Workscited by 61
  11. 2022When happy accidents spark creativity: Bringing collaborative speculation to life with generative AIcited by 9
  12. 2022Environmental and Social Sustainability of Creative-Aicited by 3

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 3; 2026: 1.

11 papers.