Deep Video Color Propagation
Simone Meyer, Victor Cornill\`ere, Abdelaziz Djelouah, Christopher, Schroers, Markus Gross

TL;DR
This paper introduces a deep learning framework for video color propagation that combines local and global strategies, improving temporal stability and semantic consistency over existing methods.
Contribution
It presents a novel deep learning approach integrating local frame-by-frame and global semantic-based color propagation strategies.
Findings
Outperforms existing video and image color propagation methods
Achieves better temporal stability and semantic consistency
Surpasses neural style transfer approaches in color propagation quality
Abstract
Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both spatially and temporally. These methods, however, are computationally expensive and do not take advantage of semantic information of the scene. In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range. Our evaluation shows the superiority of our strategy over existing video and image color propagation methods as well as neural photo-realistic style transfer approaches.
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