filename : Pra21a.pdf entry : inproceedings conference : Computer Vision and Pattern Recognition pages : 7972-7981 year : 2021 month : June title : Adaptive Convolutions for Structure-Aware Style Transfer subtitle : author : Prashanth Chandran, Gaspard Zoss, Paulo Gotardo, Markus Gross, Derek Bradley booktitle : Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ISSN/ISBN : 1558-0814 editor : publisher : IEEE publ.place : volume : issue : language : English keywords : Neural Style Transfer, Style Transfer, Modulation, GAN abstract : Style transfer between images is an artistic application of CNNs, where the 'style' of one image is transferred onto another image while preserving the latter's content. The state of the art in neural style transfer is based on Adaptive Instance Normalization (AdaIN), a technique that transfers the statistical properties of style features to a content image, and can transfer a large number of styles in real time. However, AdaIN is a global operation; thus local geometric structures in the style image are often ignored during the transfer. We propose Adaptive Convolutions (AdaConv), a generic extension of AdaIN, to allow for the simultaneous transfer of both statistical and structural styles in real time. Apart from style transfer, our method can also be readily extended to style-based image generation, and other tasks where AdaIN has already been adopted.