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GroundingBooth: Grounding Text-to-Image Customization

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arxiv 2409.08520 v3 pith:EWL2ZJMR submitted 2024-09-13 cs.CV

Zhexiao Xiong , Wei Xiong , Jing Shi , He Zhang , Yizhi Song , Nathan Jacobs This is my paper · ORCID
classification cs.CV
keywords customizationtext-to-imagegroundinggroundingboothidentitymodelobjectsspatial
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Recent approaches in text-to-image customization have primarily focused on preserving the identity of the input subject, but often fail to control the spatial location and size of objects. We introduce GroundingBooth, which achieves zero-shot, instance-level spatial grounding on both foreground subjects and background objects in the text-to-image customization task. Our proposed grounding module and subject-grounded cross-attention layer enable the creation of personalized images with accurate layout alignment, identity preservation, and strong text-image coherence. In addition, our model seamlessly supports personalization with multiple subjects. Our model shows strong results in both layout-guided image synthesis and text-to-image customization tasks. The project page is available at https://groundingbooth.github.io.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MMIG-Bench: Towards Comprehensive and Explainable Evaluation of Multi-Modal Image Generation Models

    cs.CV 2025-05 conditional novelty 6.0 of 10

    MMIG-Bench is a unified benchmark of 4,850 prompts and 1,750 reference images with a three-level evaluation suite, including the VQA-based Aspect Matching Score that correlates with human ratings.

  2. Identity-Preserving Text-to-Image Generation via Dual-Level Feature Decoupling and Expert-Guided Fusion

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A dual-level decoupling module with contrastive losses and a mixture-of-experts fusion layer improves identity preservation in subject-driven text-to-image generation.

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