Mask-Robust Image Inpainting for Imperfect Object Masks
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Solution Overview
Problem
Conventional image inpainting systems suffer from inaccuracies and inflexibilities due to their reliance on precise masks, leading to unrealistic and imprecise inpainted content when dealing with imperfect masks.
Innovation Solution
A mask-robust image inpainting system that utilizes machine-learning models trained with real-world datasets of imperfect masks, allowing for the generation of pseudo-ground-truth inpainted images and detection of leftover artifacts to refine inpainting results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional inpainting systems use precise mask requirements, then inpainting accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system changes the parameter of mask precision requirements by training the inpainting model on imperfect masks, allowing the model to learn robustness to mask imperfections while maintaining high inpainting accuracy
Solution Approach 2:
The system uses a leftover artifacts detection model that provides feedback about residual artifacts after inpainting, enabling iterative refinement to improve accuracy without requiring perfect initial masks
2Manufacturing precision
If conventional inpainting systems require accurate masks, then inpainting precision is improved, but adaptability deteriorates
Solution Approach 1:
The system changes the parameter of mask quality requirements by training on imperfect masks, enabling the model to adapt to various mask qualities while maintaining consistent inpainting precision across different scenarios
Solution Approach 2:
The inpainting model becomes universal by handling both perfect and imperfect masks, as well as different types of artifacts, making it versatile for real-world applications where mask quality varies
3Device complexity
If conventional inpainting systems use simple processing, then device complexity is reduced, but inpainting accuracy deteriorates
Solution Approach 1:
The system segments the inpainting process into multiple specialized components: an inpainting model for filling gaps, a leftover artifacts detection model for identifying residual artifacts, and an iterative refinement process, with each component optimized for its specific function to achieve high overall accuracy
4Measurement precision
If conventional inpainting systems require multiple user interactions, then inpainting accuracy is improved, but productivity deteriorates
Solution Approach 1:
The system performs self-service by automatically detecting leftover artifacts and iteratively refining the inpainting results without requiring user intervention, maintaining high accuracy while improving productivity through automation
Solution Approach 2:
The automatic artifact detection model provides continuous feedback about residual artifacts, enabling the system to self-correct and refine results iteratively without user input, achieving both high accuracy and efficiency
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for inpainting digital images utilizing mask-robust machine-learning models. In particular, in one or more embodiments, the disclosed systems obtain an initial mask for an object depicted in a digital image. Additionally, in some embodiments, the disclosed systems generate, utilizing a mask-robust inpainting machine-learning model, an inpainted image from the digital image and the initial mask. Moreover, in some implementations, the disclosed systems generate a relaxed mask that expands the initial mask. Furthermore, in some embodiments, the disclosed systems generate a modified image by compositing the inpainted image and the digital image utilizing the relaxed mask.