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Found 8 papers, 2 papers with code
Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists
We propose a method to integrate an artistic style to the brushstrokes and the painting process through collaboration with a human artist.
Getting Topology and Point Cloud Generation to Mesh
In this work, we explore the idea that effective generative models for point clouds under the autoencoding framework must acknowledge the relationship between a continuous surface, a discretized mesh, and a set of points sampled from the surface.
Learned Interpolation for 3D Generation
The typical approach for incorporating this creative process is to interpolate in a learned latent space so as to avoid the problem of generating unrealistic instances by exploiting the model's learned structure.
LucidDream: Controlled Temporally-Consistent DeepDream on Videos
In this work, we aim to propose a set of techniques to improve the controllability and aesthetic appeal when DeepDream, which uses a pre-trained neural network to modify images by hallucinating objects into them, is applied to videos.
Developing Creative AI to Generate Sculptural Objects
We explore the intersection of human and machine creativity by generating sculptural objects through machine learning.
The Myths of Our Time: Fake News
While the purpose of most fake news is misinformation and political propaganda, our team sees it as a new type of myth that is created by people in the age of internet identities and artificial intelligence.
Machinic Surrogates: Human-Machine Relationships in Computational Creativity
This paper uses examples from art and design to argue that this frame is incomplete as it fails to acknowledge the socio-technical nature of AI systems, and the different human agencies involved in their design, implementation, and operation.
Hallucinating Point Cloud into 3D Sculptural Object
Our approach extends DeepDream from images to 3D point clouds.
