Both geometry and texture are fundamental aspects of visual style. Existing style transfer methods, however, primarily focus on texture, almost entirely ignoring geometry. We propose deformable style transfer (DST), an optimization-based approach that jointly stylizes the texture and geometry of a content image to better match a style image. Unlike previous geometry-aware stylization methods, our approach is neither restricted to a particular domain (such as human faces), nor does it require training sets of matching style/content pairs. We demonstrate our method on a diverse set of content and style images including portraits, animals, objects, scenes, and paintings. Code has been made publicly available at https://github.com/sunniesuhyoung/DST.
@article{arxiv.2003.11038,
title = {Deformable Style Transfer},
author = {Sunnie S. Y. Kim and Nicholas Kolkin and Jason Salavon and Gregory Shakhnarovich},
journal= {arXiv preprint arXiv:2003.11038},
year = {2020}
}
Comments
ECCV 2020 (21 pages, 11 figures including the supplementary material)