There is a growing recognition that artists use valuable ways to understand and work with cognitive and perceptual mechanisms to convey desired experiences and narrative in their created artworks (DiPaola et al., 2010; Zeki, 2001). This paper documents our attempt to computationally model the creative process of a portrait painter, who relies on understanding human traits (i.e., personality and emotions) to inform their art. Our system includes an empathic conversational interaction component to capture the dominant personality category of the user and a generative AI Portraiture system that uses this categorization to create a personalized stylization of the user's portrait. This paper includes the description of our systems and the real-time interaction results obtained during the demonstration session of the NeurIPS 2019 Conference.

The common view that our creativity is what makes us uniquely human suggests that incorporating research on human creativity into generative deep learning techniques might be a fruitful avenue for making their outputs more compelling and human-like. Using an original synthesis of Deep Dream-based convolutional neural networks and cognitive based computational art rendering systems, we show how honing theory, intrinsic motivation, and the notion of a 'seed incident' can be implemented computationally, and demonstrate their impact on the resulting generative art. Conversely, we discuss how explorations in deep learn-ing convolutional neural net generative systems can inform our understanding of human creativity. We conclude with ideas for further cross-fertilization between AI based computational creativity and psychology of creativity.

SIAT professors Steve DiPaola and Kate Hennessy, together with Taylor Owen from UBC's journalism school, received a Google/Knight Foundation grant to carry out the research. They presented the work to international journalists at a Journalism 360 demo event honoring grantees in New York on July 24, and the next day at a full conference held across the street from the New York Times headquarters. "Our goal is to create a working technique that would be much better at conveying emotional and knowledge information than current anonymization techniques," says DiPaola, a pioneer in AI/VR facial recognition processes. Based on its research, the team has created an updated pixelating technique using an AI "painting" approach to anonymization. "When artists paint a portrait, they try to convey the subject's outer and inner resemblance," says DiPaola, who heads SFU's Interactive Visualization Lab.

A perceived limitation of evolutionary art and design algorithms is that they rely on human intervention; the artist selects the most aesthetically pleasing variants of one generation to produce the next. This paper discusses how computer generated art and design can become more creatively human-like with respect to both process and outcome. As an example of a step in this direction, we present an algorithm that overcomes the above limitation by employing an automatic fitness function. The goal is to evolve abstract portraits of Darwin, using our 2nd generation fitness function which rewards genomes that not just produce a likeness of Darwin but exhibit certain strategies characteristic of human artists. We note that in human creativity, change is less choosing amongst randomly generated variants and more capitalizing on the associative structure of a conceptual network to hone in on a vision. We discuss how to achieve this fluidity algorithmically.