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From Missing Pieces to Masterpieces: Image Completion With Context-Adaptive Diffusion.

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Summary

This summary is machine-generated.

ConFill improves image completion by aligning known and unknown image regions using a Context-Adaptive Discrepancy (CAD) model. This novel framework ensures seamless integration and enhanced detail in generated content, outperforming existing methods.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Image completion is difficult, with diffusion models struggling to blend generated content coherently with existing image parts.
Existing methods lack explicit spatial and semantic alignment, leading to inconsistencies and poor integration.

Purpose of the Study:

To introduce ConFill, a novel framework for high-fidelity image completion.
To address the coherence and integration challenges in diffusion-based image completion.

Main Methods:

Developed a Context-Adaptive Discrepancy (CAD) model to align intermediate distributions of known and unknown image regions.
Implemented a Dynamic Sampling mechanism to adaptively refine sampling rates in complex regions.

Main Results:

ConFill progressively reduces discrepancies between generated and original image content at each diffusion step.
The Dynamic Sampling mechanism enhances detail and integration in restored areas.
Extensive experiments show ConFill surpasses current state-of-the-art image completion methods.

Conclusions:

ConFill establishes a new benchmark for image completion by achieving contextually aligned and seamlessly integrated results.
The proposed CAD model and Dynamic Sampling mechanism effectively tackle coherence issues in generative models.