Human-in-Loop Computational Creativity

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With recent advances in deep learning computational creativity has come to prominence as an area of study. Current systems ranging from style transfer networks, creative sketch generation [1] and portrait art [2][3][4] can be categorized as either human as a curator, human curatorship as data source or human as a collaborator [5]. These systems lack a degree of creative control from the human as either the human provides an input (a starting line for a sketch) and the system completes the sketch, or the system has an output and the human acts as a curator. In both cases the human is unable to exercise control over the process, instead being limited to having control at the beginning or end of it. To solve this a Human-In-Loop (HIL) system is proposed that would allow for a human to modify or constrain the outputs of the system based on their creativity. Human faces are a common focus of generative AI systems and computational creativity. The reason for this is that they have high degree of regularity. Cartoons also have high degree of regularity as will be explored later in the proposal. Combined with their structurally simple and spare nature they are an excellent medium through which the application of HIL networks to computational creativity can be explored. As cartoons can generated as a series of connected parts it also provides a starting point to solve the problem of generating something authentically new from a learned distribution through unique combinations of parts.