ML Anthology

Deep Exemplar-Based Video Colorization

Bo Zhang, Mingming He, Jing Liao, Pedro V. Sander, Lu Yuan, Amine Bermak, Dong Chen
CVPR 2019
doi:10.1109/CVPR.2019.00824 /cvpr/2019/zhang2019cvpr-deep-b/

Abstract

This paper presents the first end-to-end network for exemplar-based video colorization. The main challenge is to achieve temporal consistency while remaining faithful to the reference style. To address this issue, we introduce a recurrent framework that unifies the semantic correspondence and color propagation steps. Both steps allow a provided reference image to guide the colorization of every frame, thus reducing accumulated propagation errors. Video frames are colorized in sequence based on the colorization history, and its coherency is further enforced by the temporal consistency loss. All of these components, learned end-to-end, help produce realistic videos with good temporal stability. Experiments show our result is superior to the state-of-the-art methods both quantitatively and qualitatively.

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Cite

Text

Zhang et al. "Deep Exemplar-Based Video Colorization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00824

Markdown

[Zhang et al. "Deep Exemplar-Based Video Colorization." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/zhang2019cvpr-deep-b/) doi:10.1109/CVPR.2019.00824

BibTeX

@inproceedings{zhang2019cvpr-deep-b,
  title     = {{Deep Exemplar-Based Video Colorization}},
  author    = {Zhang, Bo and He, Mingming and Liao, Jing and Sander, Pedro V. and Yuan, Lu and Bermak, Amine and Chen, Dong},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00824},
  url       = {https://mlanthology.org/cvpr/2019/zhang2019cvpr-deep-b/}
}