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AI illustrator aims to automatically design visually appealing images forbooks to provoke rich thoughts and emotions. To achieve this goal, we propose aframework for translating raw descriptions with complex semantics intosemantically corresponding images. The main challenge lies in the complexity ofthe semantics of raw descriptions, which may be hard to be visualized (e.g.,"gloomy" or "Asian"). It usually poses challenges for existing methods tohandle such descriptions. To address this issue, we propose a Prompt-basedCross-Modal Generation Framework (PCM-Frame) to leverage two powerfulpre-trained models, including CLIP and StyleGAN. Our framework consists of twocomponents: a projection module from Text Embeddings to Image Embeddings basedon prompts, and an adapted image generation module built on StyleGAN whichtakes Image Embeddings as inputs and is trained by combined semanticconsistency losses. To bridge the gap between realistic images and illustrationdesigns, we further adopt a stylization model as post-processing in ourframework for better visual effects. Benefiting from the pre-trained models,our method can handle complex descriptions and does not require external paireddata for training. Furthermore, we have built a benchmark that consists of 200raw descriptions. We conduct a user study to demonstrate our superiority overthe competing methods with complicated texts. We release our code athttps://github.com/researchmm/AI_Illustrator.

AI Illustrator: Translating Raw Descriptions into Images by Prompt-based Cross-Modal Generation