Relevant content for the tag: creativity
Our lab has extensive experience in using different sensing technology including eye tracking and facial emotion recognition (DiPaola et al 2013), as well as gesture tracking and heart rate and EDA bio sensing (Song & DiPaola, 2015) to affect generative computer graphics systems.
Bringing together an interdisciplinary team, we created a wholly new AI technique to anonymize interview subjects and scenes in regular and 360 videos to create a technique that would be much better at conveying emotional and knowledge information than current anonymization techniques.
Using Cognitive Science as a basis for our work, we attempt to model aspects of human creativity in AI. Specially we are using Neural Networks (and evolutionary systems) in the form of Deep Learning, CNNs, RNNs and other modern techniques to model aspects of human expression and creativity.
What is abstraction? Can you use AI techniques to model the semantics of an idea, object, or entity, where that understanding allows for abstraction of the meaning? We use several AI techniques including genetic programming, Neural Nets and Deep Learning to explore abstraction in its many forms. Mainly here in the visual and narrative arts.
This research uses creative evolutionary systems to explore computer creativity for various applications (in our first pass – evolving a family of abstract portrait painter programs). We use relatively new form of Genetic Programming (GP) called Cartesian Genetic Programming (CGP) first developed by Julian Miller .
Portrait artists and painters in general have over centuries developed, a little understood, intuitive and open methodology that exploits cognitive mechanisms in the human perception and visual system.
Using new visual computer modelling techniques, we show that artists use vision based techniques (lost and found edges, center of focus techniques) to guide the eye path of the viewer through their paintings in significant ways.