Search Results for author:
Found 11 papers, 8 papers with code
Aligning and Projecting Images to Class-conditional Generative Networks
We present a method for projecting an input image into the space of a class-conditional generative neural network.
Scaling up GANs for Text-to-Image Synthesis
From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models.
Ranked #18 on
Image Generation
on ImageNet 256x256
Neural Neighbor Style Transfer
We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer.
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging
Neural networks have shown great abilities in estimating depth from a single image.
Ranked #1 on
Monocular Depth Estimation
on IBims-1
Transforming and Projecting Images into Class-conditional Generative Networks
We present a method for projecting an input image into the space of a class-conditional generative neural network.
GANSpace: Discovering Interpretable GAN Controls
This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day.
A Dataset of Flash and Ambient Illumination Pairs from the Crowd
We present a dataset of thousands of ambient and flash illumination pairs to enable studying flash photography and other applications that can benefit from having separate illuminations.
Deep Painterly Harmonization
Copying an element from a photo and pasting it into a painting is a challenging task.
Graphics
Deep Photo Style Transfer
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style.
Automatic Photo Adjustment Using Deep Neural Networks
Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics.
Handling Noise in Single Image Deblurring Using Directional Filters
Based on this observation, our method applies a series of directional filters at different orientations to the input image, and estimates an accurate Radon transform of the blur kernel from each filtered image.
