TL;DR
Experiments indicate that the proposed front end of Specific Object-of-Interest Imaging improves object-region reconstruction and boundary stability for several inpainting backbones, although the gains are not uniform across all metrics or categories.
This paper proposes a zero-shot framework for constrained latent inpainting with a frozen pretrained Stable Diffusion model, requiring no task-specific training or model fine-tuning and demonstrates effective object removal and context-consistent replacement content.
This work proposes a subject clarity outpainting framework that combines vision-language model (VLM)-guided semantic conditioning with multiscale wavelet supervision for subject-localized detail preservation and develops a subject-centric data curation pipeline that constructs subject-intersecting outpainting pairs fro...
Both numerical results and visual evaluations confirm that the proposed framework provides a balanced solution for preserving structural details while maintaining perceptual realism in image inpainting applications.
FUSED combines low-level forensic cues with high-level semantic features using a sparsely-gated Mixture-of-Experts architecture, enabling the model to adaptively prioritize the most relevant signal for each token.
This paper systematically reviews existing AiOIR techniques and provides an in-depth analysis of their innovations in architectural evolution and feature learning paradigms, and critically evaluates the bottlenecks and challenges encountered by current models.
A novel image inpainting framework based on a Multi-Scale Parallel Dense Connection Network (MSPDCN) with holistically nested edge detection first employed to extract structural priors and estimate edge information of missing regions, which provides guidance for subsequent reconstruction and alleviates boundary blurrin...
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