Relevant content for the tag: creativity

Publications

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Framework for a Bio-Responsive VR for Interactive Real-time Environments and Interactives
Conference Proceedings: Electronic Visualisation and the Arts, British Computer Society, July 2017

M. Song, S. DiPaola

London, UK


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Saliency-Based Artistic Abstraction With Deep Learning and Regression Trees
Journal Article: Journal of Imaging Science and Technology, 2017
Volume 61, Issue 6.

H. Shakeri, M. Nixon, S. DiPaola

Society for Imaging Science and Technology


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Moving Collaborations: A Critical Inquiry Into Designing Creative Interactive Systems for Choreography
Journal Article: EAI Endorsed Transactions on Creative Technologies, 2016
Volume 3, Number 6

K. Carlson, T. Schiphorst, S. DiPaola

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Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity
Conference Proceedings: International Conference on Computational Creativity, 2016

G. McCaig, S. DiPaola, L. Gabora

Paris, France


Research

BioSensing 2D / 3D / VR Systems

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.


Deep Learning AI Creativity For Visuals / Words

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.


Cognitive (AI) Based Abstraction

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.


Evolving Creative Computer Programs w/Genetic Programming

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 .


Painterly NPR Project

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.


Rembrandt / Vision Science Work

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.