TextCenGen: Attention-Guided Text-Centric Background Adaptation for Text-to-Image Generation
<|reference_start|>TextCenGen: Attention-Guided Text-Centric Background Adaptation for Text-to-Image Generation: Recent advancements in Text-to-image (T2I) generation have witnessed a shift from adapting text to fixed backgrounds to creating images around text. Traditional approaches are often limited to generate layouts within static images for effective text placement. Our proposed approach, TextCenGen, introduces a dynamic adaptation of the blank region for text-friendly image generation, emphasizing text-centric design and visual harmony generation. Our method employs force-directed attention guidance in T2I models to generate images that strategically reserve whitespace for pre-defined text areas, even for text or icons at the golden ratio. Observing how cross-attention maps affect object placement, we detect and repel conflicting objects using a force-directed graph approach, combined with a Spatial Excluding Cross-Attention Constraint for smooth attention in whitespace areas. As a novel task in graphic design, experiments indicate that TextCenGen outperforms existing methods with more harmonious compositions. Furthermore, our method significantly enhances T2I model outcomes on our specially collected prompt datasets, catering to varied text positions. These results demonstrate the efficacy of TextCenGen in creating more harmonious and integrated text-image compositions.<|reference_end|>
@article{liang2024textcengen:,
    title={TextCenGen: Attention-Guided Text-Centric Background Adaptation for
  Text-to-Image Generation},
    author={Tianyi Liang, Jiangqi Liu, Sicheng Song, Shiqi Jiang, Yifei Huang,
  Changbo Wang, Chenhui Li},
    journal={arXiv preprint arXiv:2404.11824},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11824},
    primaryClass={cs.CV}
}