Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation
This addresses the need for text-guided image colorization in creative and design applications, representing a novel integration of text and color generation.
The paper tackles the problem of generating multiple color palettes from text descriptions and using them to colorize grayscale images, achieving results where people preferred the generated palettes over ground truth ones.
This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generated color palette. In contrast to existing approaches, our model can understand rich text, whether it is a single word, a phrase, or a sentence, and generate multiple possible palettes from it. For this task, we introduce our manually curated dataset called Palette-and-Text (PAT). Our proposed model called Text2Colors consists of two conditional generative adversarial networks: the text-to-palette generation networks and the palette-based colorization networks. The former captures the semantics of the text input and produce relevant color palettes. The latter colorizes a grayscale image using the generated color palette. Our evaluation results show that people preferred our generated palettes over ground truth palettes and that our model can effectively reflect the given palette when colorizing an image.