  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.
