CSF-Net: Context-Semantic Fusion Network for Large Mask Inpainting

The recent introduction of CSF-Net, a context-semantic fusion network, marks a significant advancement in the field of image inpainting. This transformer-based framework addresses the challenges of large-mask inpainting by leveraging a pretrained Amodal Completion model to generate structure-aware candidates, which serve as semantic priors for missing regions. The integration of these candidates with contextual features results in improved inpainting quality, characterized by enhanced structural accuracy and semantic consistency. Notably, CSF-Net can be seamlessly incorporated into existing inpainting models without requiring architectural modifications, making it a versatile tool for developers. Extensive experiments conducted on the Places365 and COCOA datasets have demonstrated that CSF-Net effectively reduces object hallucination while enhancing visual realism and semantic alignment, underscoring its potential impact on various applications in computer vision.