Chenyang LEI
  • Home

Fully Automatic Video Colorization with
​Self-Regularization and Diversity

Chenyang Lei, Qifeng Chen 
HKUST 
CVPR, 2019
Picture
Abstract
We present a fully automatic approach to video colorization with self-regularization and diversity. Our model contains a colorization network for video frame colorization and a refinement network for spatio-temporal color refinement. Without any labeled data, both networks can be trained with self-regularized losses defined in bilateral and temporal space. The bilateral loss enforces color consistency between neighboring pixels in a bilateral space and the temporal loss imposes constraints between corresponding pixels in two nearby frames. While video colorization is a multi-modal problem, our method uses a perceptual loss with diversity to differentiate various modes in the solution space. Perceptual experiments demonstrate that our approach outperforms state-of-the-art approaches on fully automatic video colorization. 
Video
video link: https://cqf.io/supplement/Fully_Automatic_Video_Colorization_CVPR2019.mp4
Material
📕Paper      💻Code
Bibtex
If you use our code or paper, please cite:
​
@InProceedings{Lei_2019_CVPR,

author = {Lei, Chenyang and Chen, Qifeng},
title = {Fully Automatic Video Colorization With Self-Regularization and Diversity},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
Powered by 通过可定制的模板创建自己独特的网站。
  • Home