51 citations · 73 across the 10 of their papers we have counts for
3 papers · 1 filter
Generative Models: What Do They Know? Do They Know Things? Let's Find Out!
Xiaodan Du, Nicholas Kolkin, Greg Shakhnarovich +1
Generative models excel at mimicking real scenes, suggesting they might inherently encode important intrinsic scene properties. In this paper, we aim to explore the following key q…
DIFF-NST: Diffusion Interleaving For deFormable Neural Style Transfer
Dan Ruta, Gemma Canet Tarrés, Andrew Gilbert +3
Neural Style Transfer (NST) is the field of study applying neural techniques to modify the artistic appearance of a content image to match the style of a reference style image. Tra…
NeAT: Neural Artistic Tracing for Beautiful Style Transfer
Dan Ruta, Andrew Gilbert, John Collomosse +2
Style transfer is the task of reproducing the semantic contents of a source image in the artistic style of a second target image. In this paper, we present NeAT, a new state-of-the…