Style Transfer With Auxiliary Limited Pairing

David Futschik
CTU in Prague, FEE
Michal Kučera
CTU in Prague, FEE
Michal Lukáč
Adobe Research
Zhaowen Wang
Adobe Research
Eli Shechtman
Adobe Research
Daniel Sýkora
CTU in Prague, FEE


We present an approach to example-based stylization of images that uses a single pair of a source image and its stylized counterpart. We demonstrate how to train an image translation network that can perform real-time semantically meaningful style transfer to a set of target images with similar content as the source image. A key added value of our approach is that it considers also consistency of target images during training. Although those have no stylized counterparts, we constrain the translation to keep the statistics of neural responses compatible with those extracted from the stylized source. In contrast to concurrent techniques that use a similar input, our approach better preserves important visual characteristics of the source style and can deliver temporally stable results without the need to explicitly handle temporal consistency. We demonstrate its practical utility on various applications including video stylization, style transfer to panoramas, faces, and 3D models.

Full Text     BibTeX

Computer Graphics Forum 40(2):563–573, 2021

(Eurographics 2021, Vienna, Austria, May 2021)


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