End-to-End Facial Expression and Pose Transfer for Scene Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction and specialized knowledge to perform edits, as they operate on a pixel level rather than a semantic level, making it difficult to maintain real-world conditions during modifications.
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 for 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
1Ease of operation
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 system segments the image into semantic regions (sky, ground, objects, etc.) and allows users to edit at the region level rather than pixel level. The machine learning model automatically identifies and segments meaningful areas, enabling intuitive selection and manipulation of semantic regions through simple user actions like clicking or hovering over desired areas.
Solution Approach 2:
The patent introduces an intermediary machine learning model that translates between user intent (semantic-level commands) and pixel-level modifications. This intermediary automatically handles the complex pixel manipulation tasks based on high-level user instructions, eliminating the need for users to directly manage pixel-level complexity.
2Adaptability or versatility
If pixel-level editing is used, then precise control over image details is possible, but the system cannot maintain real-world conditions and object relationships
Solution Approach 1:
The system performs preliminary actions by pre-processing the image to identify semantic regions, object boundaries, and spatial relationships before user editing occurs. The machine learning model prepares the image structure in advance, creating a semantic map that preserves real-world conditions and enables edits that automatically maintain consistency with the original scene geometry and lighting.
Solution Approach 2:
The patent applies local quality by allowing different editing operations on different semantic regions while maintaining their specific properties. Each region can be edited with appropriate constraints and transformations that preserve its real-world characteristics (e.g., sky regions maintain atmospheric perspective, ground regions maintain perspective convergence).
3Productivity
If traditional image editing workflows are used, then comprehensive control is achieved, but significant user interaction and time are required
Solution Approach 1:
The system provides self-service by automatically performing preparatory editing tasks without requiring user input. The machine learning model autonomously identifies edit opportunities, suggests modifications, and executes routine operations based on the overall user intent, significantly reducing the number of user interactions needed while maintaining comprehensive editing control.
4Ease of operation
If semantic-level editing is implemented, then ease of use improves, but the system complexity increases due to machine learning models
Solution Approach 1:
The patent extracts the complex machine learning functionality into a separate, specialized component that operates independently from the user interface layer. This extraction allows the main editing system to remain simple and intuitive, while the embedded AI model handles the complex semantic understanding and translation tasks in the background.
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.