Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unrealistic artifacts, which should not happen in real photographs. We introduce a theoretically sound correction to the network architecture that remarkably enhances photorealism and faithfully transfers the style. The key ingredient of our method is wavelet transforms that naturally fits in deep networks. We propose a wavelet corrected transfer based on whitening and coloring transforms (WCT2) that allows features to preserve their structural information and statistical properties of VGG feature space during stylization. This is the first and the only end-to-end model that can stylize a 1024x1024 resolution image in 4.7 seconds, giving a pleasing and photorealistic quality without any post-processing. Last but not least, our model provides a stable video stylization without temporal constraints. Our code, generated images, pre-trained models and supplementary documents are all available at https://github.com/ClovaAI/WCT2.
|Title of host publication||Proceedings - 2019 International Conference on Computer Vision, ICCV 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|Publication status||Published - 2019 Oct|
|Event||17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of|
Duration: 2019 Oct 27 → 2019 Nov 2
|Name||Proceedings of the IEEE International Conference on Computer Vision|
|Conference||17th IEEE/CVF International Conference on Computer Vision, ICCV 2019|
|Country/Territory||Korea, Republic of|
|Period||19/10/27 → 19/11/2|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition