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arXiv · 1908.01311

Fully Automatic Video Colorization with Self-Regularization and Diversity

Chenyang Lei·Qifeng Chen
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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 spatiotemporal 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. The results are shown in the supplementary video at https://youtu.be/Y15uv2jnK-4

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Chenyang Lei, Qifeng Chen. 2019-08-04. Fully Automatic Video Colorization with Self-Regularization and Diversity. https://arxiv.org/abs/1908.01311

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