Human Inpainting Using Segmentation Maps for Intuitive Image Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction to perform edits on a pixel level and failing to operate intuitively or efficiently.
Innovation Solution
The implementation of a scene-based image editing system that utilizes machine learning models to process digital images, allowing for flexible and efficient editing by learning characteristics of the image, anticipating edits, and generating supplementary components that reflect real-world conditions.
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 and time consumption increase significantly
Solution Approach 1:
The system segments the image into multiple semantic regions (e.g., sky, grass, buildings, vehicles) using machine learning models. Users can then select and edit entire semantic regions with a single action rather than manually selecting individual pixels, dramatically reducing interaction time while maintaining editing precision through region-based masks.
Solution Approach 2:
The system introduces semantic region masks as an intermediary layer between user input and pixel-level editing. These masks automatically generated by ML models serve as mediators that translate high-level user intent (selecting a semantic region) into precise pixel-level editing operations, eliminating the need for manual pixel selection.
2Measurement precision
If conventional image editing systems require manual pixel selection, then editing control is precise, but ease of operation deteriorates due to complex workflows
Solution Approach 1:
The system performs automatic semantic segmentation and mask generation without requiring user intervention. The machine learning models automatically analyze the image, identify semantic regions, and create precise masks, allowing the system to serve itself in preparing editing data structures that would otherwise require complex manual user actions.
Solution Approach 2:
The system performs preliminary semantic segmentation and mask generation before the user actually initiates editing. By pre-processing the image to create structured semantic regions and masks in advance, the system prepares the editing environment so that users only need to select from pre-defined regions rather than performing complex pixel-level selections.
3Loss of information
If machine learning models process entire images for editing, then comprehensive understanding is achieved, but computational efficiency decreases
Solution Approach 1:
The system divides the entire image into multiple semantic regions and processes each region independently using separate mask generators. This segmentation approach allows parallel processing of different image portions, reducing overall computational time while maintaining comprehensive understanding of all semantic elements in the image.
Solution Approach 2:
The system generates semantic region masks selectively based on user selection rather than processing the entire image uniformly. When a user selects a specific region for editing, the system focuses computational resources on generating and refining masks for relevant regions only, avoiding unnecessary processing of unrelated image portions.
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 example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.