Deep Learning AI Creativity For Visuals / Words

Research Collaborators
Steve DiPaola , Graeme McCaig , Nilay Ozge Yalcin , Suk Kyoung Choi , Vanessa Utz , Nouf Abukhodair , Meehae Song

The goal of our research team is to model aspects of human creativity in AI using cognitive science as a basis for our work. Specially we are using Neural Networks (and evolutionary systems) in the form of Deep Learning, CNNs, RNNs and other modern techniques to attempt to replicate 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.

For a quick over view of the finding see google photo’s ALBUM. Or the video work at DiPaola’s youtube. For additional info and images see Downloads and Links on the bottom of the page.

* Please click on the images to view in full screen *

The Research
See Papers and Videos.

Setup and Results
See Papers and Videos.

Downloads and Links
Link: BICA 2019 Paper PDF: BICA 2019 Paper:Aesthetic Judgments, Movement Perception and the Neural Architecture of the Visual System
PDF: ICCC paper PDF: Deep Convolutional Networks as Models of Generalization and Blending Within Visual Creativity
PDF: EVA Paper PDF: Using Artificial Intelligence Techniques to Emulate the Creativity of a Portrait Painter
PDF: Journal Paper PDF: Using a Contextual Focus Model for an AutomaticCreativity Algorithm to Generate Art Work

Additional Image and Video Galleries
Image Repository Repository of Many AI Still Images
Deep AI Video Texture and Flow – Art Video
Additional Video AI Stills Creativity Stills using Artifical Intelligence from Movie
Additional Stills Deep Learning Art Stills.