crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity

crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity
Janet Rafner, Lotte Philipsen, Lior Noy, Jesse Lynch, Arthur Hjorth, Charles Dumas, Kristian Tylén, Blanka Zana, Sebastian Risi, Michael Mose, Carsten Bergenholtz, Jacob Sherson

CHI PLAY ’20: The Annual Symposium on Computer-Human Interaction in Play
Abstract
We present a pilot study on crea.blender, a novel co-creative game designed for large-scale, systematic assessment of distinct constructs of human creativity. Co-creative systems are systems in which humans and computers (often with Machine Learning) collaborate on a creative task. This human-computer collaboration raises questions about the relevance and level of human creativity and involvement in the process. We expand on, and explore aspects of these questions in this pilot study. We observe participants play through three different play modes in crea.blender, each aligned with established creativity assessment methods. In these modes, players “blend” existing images into new images under varying constraints. Our study indicates that crea.blender provides a playful experience, affords players a sense ofcontrol over the interface, and elicits different types of player behavior, supporting further study of the tool for use in a scalable, playful, creativity assessment.

DOI:: N/A
WEB:: https://chiplay.acm.org/2020

Remote Presentations for the The Annual Symposium on Computer-Human Interaction in Play (CHI PLAY 2020)

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