Semantic Image Reposing and Inpainting for Scene-Based Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction and specialized knowledge to edit digital images, as they operate on a pixel level rather than a semantic level, failing to maintain real-world conditions during edits.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, identifying semantic areas and generating object masks and content fills, allowing intuitive and efficient editing by treating objects as distinct units and maintaining real-world conditions without user input for preparatory steps.
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 interaction complexity and specialized knowledge requirements increase significantly
Solution Approach 1:
The patent segments the image into semantic objects (person, car, building, etc.) rather than treating it as individual pixels. Each object is identified and can be manipulated as a distinct unit, reducing the complexity of user interaction while maintaining editing precision through semantic understanding.
Solution Approach 2:
The patent introduces an intermediary layer of semantic object detection and identification between the user and the pixel-level editing operations. This intermediary translates high-level semantic commands into low-level pixel manipulations, making the system easier to operate while preserving detailed control capabilities.
2Adaptability or versatility
If conventional image editing systems require multiple user interactions for preparatory steps, then editing flexibility is maintained, but operational efficiency decreases
Solution Approach 1:
The patent performs preliminary actions by automatically detecting, segmenting, and identifying semantic objects in the image before the user initiates editing. This pre-processing step prepares the image data structure, allowing users to directly manipulate identified objects without needing to perform preparatory selection or masking operations, thereby improving operational efficiency while maintaining editing flexibility.
3Device complexity
If conventional image editing systems treat images as two-dimensional pixel arrays, then data processing simplicity is maintained, but ability to maintain real-world conditions during edits is lost
Solution Approach 1:
The patent adds a semantic dimension to the traditional two-dimensional pixel array by overlaying object masks and semantic labels. This transforms the image representation from purely spatial coordinates to include semantic meaning, enabling the system to understand and maintain real-world conditions (such as object relationships, occlusions, and scene context) while preserving the underlying pixel data structure for processing.
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.