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Video colorization is a challenging and highly ill-posed problem. Althoughrecent years have witnessed remarkable progress in single image colorization,there is relatively less research effort on video colorization and existingmethods always suffer from severe flickering artifacts (temporal inconsistency)or unsatisfying colorization performance. We address this problem from a newperspective, by jointly considering colorization and temporal consistency in aunified framework. Specifically, we propose a novel temporally consistent videocolorization framework (TCVC). TCVC effectively propagates frame-level deepfeatures in a bidirectional way to enhance the temporal consistency ofcolorization. Furthermore, TCVC introduces a self-regularization learning (SRL)scheme to minimize the prediction difference obtained with different timesteps. SRL does not require any ground-truth color videos for training and canfurther improve temporal consistency. Experiments demonstrate that our methodcan not only obtain visually pleasing colorized video, but also achieve clearlybetter temporal consistency than state-of-the-art methods.

Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning