RealText: Realistic Text Image Generation based on Glyph and Scene Aware Inpainting

摘要

Text-to-image generation models can create diverse, high-quality images, but they frequently encounter challenges in accurately rendering text within those images due to the insufficient representation of desired text. In this study, we introduce RealText, a method for generating scene text images that excels in producing precise and realistic scene text images in any language. We disentangle scene text images generation into three stages: background and glyph image generation, text deformation, and whole image generation. Initially, we utilize prompts to guide the creation of well-organized background images. By identifying optimal text placements on these backgrounds, we render the glyph images of target text using user-specified font, effectively eliminating incorrect characters. In the next stage, we propose scene sensing to perceive text carrier surfaces and viewpoints through 3D scene reconstruction using depth and normal map to apply text deformation, thereby enhancing the realism of generated images. The final stage involves generating complete image with the aid of background and glyph guidance. Thanks to glyph disentangling, scene sensing, and text inpainting, we can exert more precise control over scene text image generation process. We have developed a unified framework which supports major generation models. Extensive experiments illustrate the exceptional performance of our method in generating images with multilingual text. The codes will soon be available at https://github.com/cccvl/RealText.