DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
Developing effective visual inspection models remains challenging due to the
scarcity of defect data. While image generation models have been used to
synthesize defect images, producing highly realistic defects remains difficult.
We propose DefectFill, a novel method for realistic defect generation that
requires only a few reference defect images. It leverages a fine-tuned
inpainting diffusion model, optimized with our custom loss functions
incorporating defect, object, and attention terms. It enables precise capture
of detailed, localized defect features and their seamless integration into
defect-free objects. Additionally, our Low-Fidelity Selection method further
enhances the defect sample quality. Experiments show that DefectFill generates
high-quality defect images, enabling visual inspection models to achieve
state-of-the-art performance on the MVTec AD dataset.
