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Recent advances in text-conditioned generative models have provided us withneural networks capable of creating images of astonishing quality, be theyrealistic, abstract, or even creative. These models have in common that (moreor less explicitly) they all aim to produce a high-quality one-off output givencertain conditions, and in that they are not well suited for a creativecollaboration framework. Drawing on theories from cognitive science that modelhow professional designers and artists think, we argue how this setting differsfrom the former and introduce CICADA: a Collaborative, InteractiveContext-Aware Drawing Agent. CICADA uses a vector-basedsynthesis-by-optimisation method to take a partial sketch (such as might beprovided by a user) and develop it towards a goal by adding and/or sensiblymodifying traces. Given that this topic has been scarcely explored, we alsointroduce a way to evaluate desired characteristics of a model in this contextby means of proposing a diversity measure. CICADA is shown to produce sketchesof quality comparable to a human user's, enhanced diversity and mostimportantly to be able to cope with change by continuing the sketch minding theuser's contributions in a flexible manner.

A Collaborative, Interactive and Context-Aware Drawing Agent for Co-Creative Design