Abstract
AI illustrator aims to automatically design visually appealing images for books to provoke rich thoughts and emotions. To achieve this goal, we propose a framework for translating raw descriptions with complex semantics into semantically corresponding images. The main challenge lies in the complexity of the semantics of raw descriptions, which may be hard to be visualized (\textit{e}.\textit{g}., "gloomy" or "Asian"). It usually poses challenges for existing methods to handle such descriptions. To address this issue, we propose a \textbf{P}rompt-based \textbf{C}ross-\textbf{M}odal Generation \textbf{Frame}work (PCM-Frame) to leverage two powerful pre-trained models, including CLIP and StyleGAN. Our framework consists of two components: a projection module from \textit{Text Embedding}s to \textit{Image Embedding}s based on prompts, and an adapted image generation module built on StyleGAN which takes \textit{Image Embedding}s as inputs and is trained by combined semantic consistency losses. To bridge the gap between realistic images and illustration designs, we further adopt a stylization model as post-processing in our framework for better visual effects. Benefiting from the pre-trained models, our method can handle complex descriptions and does not require external paired data for training. Furthermore, we have built a benchmark that consists of 200 raw descriptions. We conduct a user study to demonstrate our superiority over the competing methods with complicated texts. We release our code at this https URL\_Illustrator}{this https URL\_Illustrator
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URL
https://arxiv.org/abs/2209.03160
