Arbitrary-Scale Image Synthesis

Abstract

Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales. However, these approaches are either limited to a set of discrete scales or struggle to maintain good perceptual quality at the scales for which the model is not trained explicitly. We propose the design of scale-consistent positional encodings invariant to our generator’s layers transformations. This enables the generation of arbitrary-scale images even at scales unseen during training. Moreover, we incorporate novel inter-scale augmentations into our pipeline and partial generation training to facilitate the synthesis of consistent images at arbitrary scales. Lastly, we show competitive results for a continuum of scales on various commonly used datasets for image synthesis.

Publication
In Conference on Computer Vision and Pattern Recognition, CVPR 2022
Evangelos Ntavelis
Evangelos Ntavelis
PhD Student, ETH Zurich
Mohamad Shahbazi
Mohamad Shahbazi
PhD Student, ETH Zurich
Martin Danelljan
Martin Danelljan
Researcher

Researcher in Computer Vision and Machine Learning at ETH Zurich