High-Resolution Daytime Translation Without Domain Labels

03/19/2020
by   Ivan Anokhin, et al.
2

Modeling daytime changes in high resolution photographs, e.g., re-rendering the same scene under different illuminations typical for day, night, or dawn, is a challenging image manipulation task. We present the high-resolution daytime translation (HiDT) model for this task. HiDT combines a generative image-to-image model and a new upsampling scheme that allows to apply image translation at high resolution. The model demonstrates competitive results in terms of both commonly used GAN metrics and human evaluation. Uniquely, this good performance comes as a result of training on a dataset of still landscape images with no daytime labels available. Our results are available at https://saic-mdal.github.io/HiDT/.

READ FULL TEXT

page 1

page 5

page 6

page 7

page 8

research
05/27/2021

Efficient High-Resolution Image-to-Image Translation using Multi-Scale Gradient U-Net

Recently, Conditional Generative Adversarial Network (Conditional GAN) h...
research
08/23/2022

Ultra-high-resolution unpaired stain transformation via Kernelized Instance Normalization

While hematoxylin and eosin (H E) is a standard staining procedure, im...
research
09/26/2021

ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation

Recently, there has been an increasing interest in image editing methods...
research
03/15/2022

Multi-Curve Translator for Real-Time High-Resolution Image-to-Image Translation

The dominant image-to-image translation methods are based on fully convo...
research
07/04/2022

Harmonizer: Learning to Perform White-Box Image and Video Harmonization

Recent works on image harmonization solve the problem as a pixel-wise im...
research
11/16/2019

On Space-spectrum Uncertainty Analysis for Coded Aperture Systems

We introduce and analyze the concept of space-spectrum uncertainty for c...
research
12/03/2020

Full-Resolution Correspondence Learning for Image Translation

We present the full-resolution correspondence learning for cross-domain ...

Please sign up or login with your details

Forgot password? Click here to reset