Automatic Controllable Colorization via Imagination
This work addresses the need for more controllable and editable colorization tools for users in image processing, though it is incremental as it builds on existing methods with a novel imagination module.
The authors tackled the problem of automatic colorization by introducing a framework that enables iterative editing and modifications, achieving superior editability and flexibility compared to existing algorithms.
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.