Abstract
Given a random pair of images, a universal style transfer method extracts the feel from a reference image to synthesize an output based on the look of a content image. Recent algorithms based on second-order statistics, however, are either computationally expensive or prone to generate artifacts due to the trade-off between image quality and runtime performance. In this work, we present an approach for universal style transfer that learns the transformation matrix in a data-driven fashion. Our algorithm is efficient yet flexible to transfer different levels of styles with the same auto-encoder network. It also produces stable video style transfer results due to the preservation of the content affinity. In addition, we propose a linear propagation module to enable a feed-forward network for photo-realistic style transfer. We demonstrate the effectiveness of our approach on three tasks: Artistic style, photo-realistic and video style transfer, with comparisons to state-of-the-art methods.
Original language | English |
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Title of host publication | Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
Publisher | IEEE Computer Society |
Pages | 3804-3812 |
Number of pages | 9 |
ISBN (Electronic) | 9781728132938 |
DOIs | |
Publication status | Published - 2019 Jun |
Event | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States Duration: 2019 Jun 16 → 2019 Jun 20 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Volume | 2019-June |
ISSN (Print) | 1063-6919 |
Conference
Conference | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 |
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Country | United States |
City | Long Beach |
Period | 19/6/16 → 19/6/20 |
Bibliographical note
Funding Information:Acknowledgement This work is supported in part by the NSF CAREER Grant #1149783, and gifts from Adobe, Verisk, and NEC.
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition