5 papers
Improving Masked Style Transfer using Blended Partial Convolution
Seyed Hadi Seyed, Ayberk Cansever, David Hart
Artistic style transfer has long been possible with the advancements of convolution- and transformer-based neural networks. Most algorithms apply the artistic style transfer to the…
Optimization-Free Style Transfer for 3D Gaussian Splats
Raphael Du Sablon, David Hart
The task of style transfer for 3D Gaussian splats has been explored in many previous works, but these require reconstructing or fine-tuning the splat while incorporating style info…
Interpolated SelectionConv for Spherical Images and Surfaces
David Hart, Michael Whitney, Bryan Morse
We present a new and general framework for convolutional neural network operations on spherical (or omnidirectional) images. Our approach represents the surface as a graph of conne…
SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data
David Hart, Michael Whitney, Bryan Morse
Convolutional Neural Networks have revolutionized vision applications. There are image domains and representations, however, that cannot be handled by standard CNNs (e.g., spherica…
Style Transfer for Light Field Photography
David Hart, Jessica Greenland, Bryan Morse
As light field images continue to increase in use and application, it becomes necessary to adapt existing image processing methods to this unique form of photography. In this paper…