Relevant content for the tag: generative-design
Publications
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
London, UK
Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity
Conference Proceedings: International Conference on Computational Creativity, 2016
Paris, France
Using Artificial Intelligence Techniques to Emulate the Creativity of a Portrait Painter
Conference Proceedings: Electronic Visualisation and the Arts, British Computer Society, 2016
8 pages
London, UK
A Creative Artificial Intelligence System to Investigate User Experience, Affect, Emotion and Creativity
Conference Proceedings: Electronic Visualisation and the Arts, 2015
8 pages
London, UK
British Computer Society
Exploring Different Ways of Navigating Emotionally-responsive Artwork in Immersive Virtual Environments
Conference Proceedings: Electronic Visualisation and the Arts, 2015
8 pages
London, UK
British Computer Society
Enhancing Viewer's Emotional Connections to the Traditional Art Creative Process via an AI Interactive System
Conference Proceedings: Electronic Visualisation and the Arts, Florence, 2015
8 pages
Florence, Italy
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.
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 .
GenFace - Exploring FaceSpace with Genetic Algorithms
Imagine an -dimensional space describing every conceivable humanoid face, where each dimension represents a different facial characteristic. Within this continuous space, it would be possible to traverse a path from any face to any other face, morphing through locally similar faces along that path. We will describe and demonstrate a development system we have created to explore what it means to ‘surf’through face space. We will present our investigation of the relationships between facial types and how this understanding can be used to create new communication and expression systems.