Abstract
Casual creators are creativity support tools designed for non-experts to have fun with while they create, rather than for serious creative production. We discuss here how we adapted and enhanced an evolutionary art approach for casual creation. Employing a fun-first methodology for the app design, we improved image production speed and the quality of randomly generated images. We further employed machine vision techniques for image categorisation and clustering, and designed a user interface for fast, fun image generation, adhering to numerous principles arising from the study of casual creators. We describe the implementation and experimentation performed during the first stage of development, and evaluate the app in terms of efficiency, image quality, feedback quality and the potential for users to have fun. We conclude with a description of how the app, which is destined for public release, will also be used as a research platform and as part of an art installation.
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Acknowledgements
We would like to thank the anonymous reviewers for their comments. Many thanks also to the SensiLab colleagues and IGGI students who gave such detailed, insightful and supportive feedback on the Art Done Quick app.
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Colton, S., McCormack, J., Berns, S., Petrovskaya, E., Cook, M. (2020). Adapting and Enhancing Evolutionary Art for Casual Creation. In: Romero, J., Ekárt, A., Martins, T., Correia, J. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2020. Lecture Notes in Computer Science(), vol 12103. Springer, Cham. https://doi.org/10.1007/978-3-030-43859-3_2
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