Trash to Treasure: Using text-to-image models to inform the design of physical artefacts

Amy Smith, Hope Schroeder, Amy Smith, Ziv Epstein, Mike Cook, Simon Colton, Andrew Lippman

Keywords: generative AI, design, creativity, creative tools, pilot study

TL;DR: A pilot study where we use Stable Diffusion during a sculpture-making activity and find that 1) generated imagery informs sculpture and 2) prompting style is related to the participant's design stage

Abstract:

Text-to-image generative models have recently exploded in popularity and accessibility. Yet so far, use of these models in creative tasks that bridge the 2D digital world and the creation of physical artefacts has been understudied.
We conduct a pilot study to investigate if and how text-to-image models can be used to assist in upstream tasks within the creative process, such as ideation and visualization, prior to a sculpture-making activity. Thirty participants selected sculpture-making materials and generated three images using the Stable Diffusion text-to-image generator, each with text prompts of their choice, with the aim of informing and then creating a physical sculpture. The majority of participants (23/30) reported that the generated images informed their sculptures, and 28/30 reported interest in using text-to-image models to help them in a creative task in the future. We identify several prompt engineering strategies and find that a participant's prompting strategy relates to their stage in the creative process. We discuss how our findings can inform support for users at different stages of the design process and for using text-to-image models for physical artefact design.

Submission Type: archival

Presentation Type: onsite

Presenter: Amy Smith