Video Colorization with Pre-trained Text-to-Image Diffusion Models
Hanyuan Liu Minshan Xie Jinbo Xing Chengze Li Tien-Tsin Wong
Video colorization is a challenging task that involves inferring plausible
and temporally consistent colors for grayscale frames. In this paper, we
present ColorDiffuser, an adaptation of a pre-trained text-to-image latent
diffusion model for video colorization. With the proposed adapter-based
approach, we repropose the pre-trained text-to-image model to accept input
grayscale video frames, with the optional text description, for video
colorization. To enhance the temporal coherence and maintain the vividness of
colorization across frames, we propose two novel techniques: the Color
Propagation Attention and Alternated Sampling Strategy. Color Propagation
Attention enables the model to refine its colorization decision based on a
reference latent frame, while Alternated Sampling Strategy captures
spatiotemporal dependencies by using the next and previous adjacent latent
frames alternatively as reference during the generative diffusion sampling
steps. This encourages bidirectional color information propagation between
adjacent video frames, leading to improved color consistency across frames. We
conduct extensive experiments on benchmark datasets, and the results
demonstrate the effectiveness of our proposed framework. The evaluations show
that ColorDiffuser achieves state-of-the-art performance in video colorization,
surpassing existing methods in terms of color fidelity, temporal consistency,
and visual quality.