Object Mask Dilation for Artifact-Reduced AI Image Inpainting
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction to perform edits at the pixel level and failing to anticipate and prepare for object-level modifications.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, generating object masks and content fills, and creating semantic scene graphs to facilitate intuitive, object-aware modifications, reducing the need for user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image editing systems perform pixel-level edits, then editing precision is maintained, but user interaction complexity increases significantly
Solution Approach 1:
The system segments the image into distinct object masks using machine learning models, allowing users to edit entire objects with simple interactions rather than individual pixels. The segmentation separates foreground objects from background, enabling object-level editing operations that maintain precision while reducing complexity.
Solution Approach 2:
The system introduces an intermediary object mask between the user and the pixel-level editing process. Users interact with the object mask as a cohesive unit, and the system automatically translates this high-level interaction into precise pixel-level modifications, eliminating the need for direct pixel manipulation.
2Ease of operation
If conventional systems require significant user interaction for edits, then editing control is maintained, but productivity decreases
Solution Approach 1:
The system performs preliminary actions by pre-processing the image to generate object masks, segment the scene into semantic areas, and prepare editing candidates before the user actually requests an edit. This preliminary processing enables the system to anticipate and prepare for edits, reducing the interactions needed during the actual editing process.
Solution Approach 2:
The system provides self-service by automatically performing scene analysis, object segmentation, and editing operations based on user intent. The machine learning models autonomously identify objects, generate masks, and execute edits without requiring users to manually control each step, thereby maintaining editing control while dramatically improving productivity.
3Adaptability or versatility
If machine learning models pre-process images to generate object masks, then editing flexibility increases, but device complexity increases
Solution Approach 1:
The system implements a universal machine learning model that performs multiple functions: scene analysis, object detection, mask generation, and semantic segmentation. This multi-functional approach increases editing flexibility while managing complexity by consolidating capabilities into a single integrated model rather than requiring separate specialized systems.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate, utilizing a segmentation neural network and without user input, object masks for objects in a digital image. The disclosed systems determine foreground and background abutting an object mask. The disclosed systems generate an expanded object mask by expanding the object mask into the foreground abutting the object mask by a first amount and expanding the object mask into the background abutting the object mask by a second amount that differs from the first amount. The disclosed systems inpaint a hole corresponding to the expanded object mask utilizing an inpainting neural network.