GroundingBooth: Grounding Text-to-Image Customization
<|reference_start|>GroundingBooth: Grounding Text-to-Image Customization: Recent studies in text-to-image customization show great success in generating personalized object variants given several images of a subject. While existing methods focus more on preserving the identity of the subject, they often fall short of controlling the spatial relationship between objects. In this work, we introduce GroundingBooth, a framework that achieves zero-shot instance-level spatial grounding on both foreground subjects and background objects in the text-to-image customization task. Our proposed text-image grounding module and masked cross-attention layer allow us to generate personalized images with both accurate layout alignment and identity preservation while maintaining text-image coherence. With such layout control, our model inherently enables the customization of multiple subjects at once. Our model is evaluated on both layout-guided image synthesis and reference-based customization tasks, showing strong results compared to existing methods. Our work is the first work to achieve a joint grounding on both subject-driven foreground generation and text-driven background generation.<|reference_end|>
@article{xiong2024groundingbooth:,
    title={GroundingBooth: Grounding Text-to-Image Customization},
    author={Zhexiao Xiong, Wei Xiong, Jing Shi, He Zhang, Yizhi Song, Nathan
  Jacobs},
    journal={arXiv preprint arXiv:2409.08520},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08520},
    primaryClass={cs.CV}
}