51 citations · 73 across the 10 of their papers we have counts for
12 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…
Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
Xiaodan Du, Raymond A. Yeh, Nicholas Kolkin +2
We propose Fast text2StyleGAN, a natural language interface that adapts pre-trained GANs for text-guided human face synthesis. Leveraging the recent advances in Contrastive Languag…
Neural Neighbor Style Transfer
Nicholas Kolkin, Michal Kucera, Sylvain Paris +3
We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our appro…
Harnessing the Conditioning Sensorium for Improved Image Translation
Cooper Nederhood, Nicholas Kolkin, Deqing Fu +1
Multi-modal domain translation typically refers to synthesizing a novel image that inherits certain localized attributes from a 'content' image (e.g. layout, semantics, or geometry…
Non-Parametric Neural Style Transfer
Nicholas Kolkin
It seems easy to imagine a photograph of the Eiffel Tower painted in the style of Vincent van Gogh's 'The Starry Night', but upon introspection it is difficult to precisely define…
Deformable Style Transfer
Sunnie S. Y. Kim, Nicholas Kolkin, Jason Salavon +1
Both geometry and texture are fundamental aspects of visual style. Existing style transfer methods, however, primarily focus on texture, almost entirely ignoring geometry. We propo…
DIODE: A Dense Indoor and Outdoor DEpth Dataset
Igor Vasiljevic, Nick Kolkin, Shanyi Zhang +8
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements. DIODE (Dense Indoor/Outdoor DEpth…
Style Transfer by Relaxed Optimal Transport and Self-Similarity
Nicholas Kolkin, Jason Salavon, Greg Shakhnarovich
Style transfer algorithms strive to render the content of one image using the style of another. We propose Style Transfer by Relaxed Optimal Transport and Self-Similarity (STROTSS)…
Training Deep Networks to be Spatially Sensitive
Nicholas Kolkin, Gregory Shakhnarovich, Eli Shechtman
In many computer vision tasks, for example saliency prediction or semantic segmentation, the desired output is a foreground map that predicts pixels where some criteria is satisfie…
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
Mohammadreza Mostajabi, Nicholas Kolkin, Gregory Shakhnarovich
We propose an approach for learning category-level semantic segmentation purely from image-level classification tags indicating presence of categories. It exploits localization cue…