iVizLab - Research Lab - Computer Modelling Human Expression/Cognition/Creativity
iVizLab is focused on computational models of human characteristics such as expression, emotion, behavior and creativity; including computer graphics based facial/character systems and AI based cognitive modelling systems.
This “Expression Based Interactive or Intelligent Visualization Lab” is grounded in the engineering of AI techniques, computer graphics, data visualization and user interfaces but also strives to encompass work from the areas of cognitive science, neuroscience, and the arts with the aim of creating more socially engaging systems that enhance communication, collaboration and learning.
iVizlab is- “An interdisciplinary research lab that strives to make computational systems bend more to the human experience by incorporating biological, cognitive and behavioral knowledge models.” -Dr. Steve DiPaola
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. We are known for modelling expression semantics and generation of visual art (stills, videos, VR) but have extended our work into expressive forms of linguistic (word based) narrative.
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. These bio-feedback systems can be used to further understand the body’s reception to generated stimulus (photos, video, VR). They can also be used in conjuncture with other systems such as physical testing and psychological evaluation to help visualize the body’s systems and responses.
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.