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HeterStyle: A Heterogeneous Video Style Transfer Application

Published: 15 October 2018 Publication History

Abstract

Video style transfer aims to synthesize a stylized video that preserves the content of a given video and is rendered in the style of a reference image.A key issue in video style transfer is how to balance video content preservation and reference style rendering, in order to avoid over-stylization with serious video content loss or under-stylization with unrecognized reference style. In this demonstration, we illustrate a novel video style transfer application, named HeterStyle, which can stylize different regions in the video with adaptive intensities.The core algorithm of HeterStyle application is our proposed heterogeneous video style transfer method, which minimizes a heterogeneous style transfer loss function considering content, style and temporal consistency in a Convolutional Neural Networks based optimization framework.With the HeterStyle application, a user can easily generate the stylized videos with good video content preservation and reference style rendering.

References

[1]
Dongdong Chen, Jing Liao, Lu Yuan, Nenghai Yu, and Gang Hua. 2017. Coherent Online Video Style Transfer. In IEEE International Conference on Computer Vision. IEEE, 1105--1114.
[2]
Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. 2016. Image style transfer using convolutional neural networks. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2414--2423.
[3]
Haozhi Huang, Hao Wang, Wenhan Luo, Lin Ma, Wenhao Jiang, Xiaolong Zhu, Zhifeng Li, and Wei Liu. 2017. Real-time neural style transfer for videos. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 7044--7052.
[4]
Manuel Ruder, Alexey Dosovitskiy, and Thomas Brox. 2016. Artistic style transfer for videos. In German Conference on Pattern Recognition. Springer, 26--36.

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Published In

cover image ACM Conferences
MM '18: Proceedings of the 26th ACM international conference on Multimedia
October 2018
2167 pages
ISBN:9781450356657
DOI:10.1145/3240508
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2018

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Author Tags

  1. convolutional neural networks
  2. heterogeneous stylization
  3. optical flow
  4. salient object detection
  5. video style transfer

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  • Demonstration

Funding Sources

  • The Fundamental Research Funds for the Central Universities
  • Collaborative Innovation Center of Novel Software Technology and Industrialization
  • National Science Foundation of China

Conference

MM '18
Sponsor:
MM '18: ACM Multimedia Conference
October 22 - 26, 2018
Seoul, Republic of Korea

Acceptance Rates

MM '18 Paper Acceptance Rate 209 of 757 submissions, 28%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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