Human Inpainting With Segmentation Maps for Simpler Image Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring deep user knowledge and multiple interactions to edit digital images, as they typically operate on a pixel level without considering the semantic meaning of image elements.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, allowing for semantic-level editing by automatically maintaining real-world conditions and reducing user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image editing systems operate on pixel level, then detailed control is achieved, but user knowledge requirement and interaction complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the image into semantically meaningful regions (e.g., sky, grass, objects) rather than treating it as individual pixels. This allows users to edit entire semantic areas with simple operations while the system automatically handles the detailed pixel-level modifications within each region, thus maintaining precision while improving ease of operation.
Solution Approach 2:
The patent introduces semantic segmentation maps as an intermediary layer between user input and pixel-level editing. Users interact with high-level semantic concepts, and the system translates these into detailed pixel modifications through the segmentation framework, reducing the knowledge barrier while maintaining editing precision.
2Manufacturing precision
If conventional image editing systems require multiple user interactions, then editing accuracy is improved, but productivity and efficiency decrease
Solution Approach 1:
The patent performs preliminary semantic segmentation and region identification automatically before user editing begins. This pre-processing organizes the image into edit-ready semantic regions, allowing users to make accurate edits in fewer interactions since the heavy lifting of understanding image structure has already been done by the system.
Solution Approach 2:
The system performs self-service by automatically maintaining real-world conditions and contextual relationships during editing. When users modify one region, the system autonomously adjusts related regions to maintain consistency, reducing the need for multiple user interactions to achieve accurate results across the entire image.
3Ease of manufacture
If image editing treats pixels as individual units, then detailed manipulation is possible, but semantic understanding and real-world condition maintenance are lost
Solution Approach 1:
The patent segments the image into semantically meaningful regions while preserving the underlying pixel data. Each segment represents a real-world entity or concept (e.g., sky, water, objects), allowing the system to maintain semantic understanding while enabling simple region-level operations. The segmentation framework bridges the gap between high-level semantic concepts and low-level pixel manipulation.
Solution Approach 2:
The patent adds a semantic dimension to traditional pixel-level editing by introducing hierarchical organization from pixels to regions to semantic concepts. This multi-dimensional approach allows users to operate at the convenient semantic level while the system maintains and manipulates pixel-level details through the intermediate regional structure.
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.