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.

该论文提出了一种新的方法来根据输入的文本生成多个颜色调色板，并根据生成的颜色调色板对给定的灰度图像着色。该模型能够理解文本的语义，并从中生成多个可能的颜色调色板。经评估结果表明，使用该模型生成的调色板优于真实的调色板，并且该模型在对图像进行着色时能够有效反映给定的调色板。