Automatic Controllable Colorization via Imagination
Xiaoyan Cong Yue Wu Qifeng Chen Chenyang Lei
We propose a framework for automatic colorization that allows for iterative
editing and modifications. The core of our framework lies in an imagination
module: by understanding the content within a grayscale image, we utilize a
pre-trained image generation model to generate multiple images that contain the
same content. These images serve as references for coloring, mimicking the
process of human experts. As the synthesized images can be imperfect or
different from the original grayscale image, we propose a Reference Refinement
Module to select the optimal reference composition. Unlike most previous
end-to-end automatic colorization algorithms, our framework allows for
iterative and localized modifications of the colorization results because we
explicitly model the coloring samples. Extensive experiments demonstrate the
superiority of our framework over existing automatic colorization algorithms in
editability and flexibility. Project page:
https://xy-cong.github.io/imagine-colorization.