AI Reposing and Semantic Image Editing for Fewer User Steps
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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, leading to rigid and cumbersome editing processes.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to pre-process digital images, identifying objects, relationships, and attributes, allowing for intuitive and efficient editing by treating semantic areas as distinct units, reducing the need for user interactions and maintaining real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional image editing systems are used, then users can perform image edits, but the process requires significant user interactions and pixel-level operations which is cumbersome and inefficient
Solution Approach 1:
The system segments the image into multiple semantic areas using machine learning models, allowing users to edit entire semantic regions (e.g., sky, grass, buildings) as unified units rather than manipulating individual pixels. This segmentation enables intuitive selection and modification of meaningful image components, dramatically reducing the complexity of user interactions while maintaining high editing productivity.
Solution Approach 2:
The system performs preliminary processing by pre-segmenting the image into semantic areas and pre-processing data structures before user interaction occurs. This preliminary action prepares the image data in advance, enabling rapid editing operations when users provide high-level commands, thereby improving editing efficiency without requiring users to engage in complex pixel-level operations.
2Adaptability or versatility
If machine learning models are used to process images as real scenes, then intuitive editing of semantic areas is enabled, but the system complexity increases
Solution Approach 1:
The system introduces machine learning models as intermediary components that automatically perform the complex task of segmenting images into semantic areas. These models act as mediators between the raw image data and the user interface, translating pixel data into meaningful semantic regions without requiring users to understand or manage the underlying complexity. This intermediary layer enables flexible and intuitive editing while encapsulating system complexity within the automated processing pipeline.
3Ease of operation
If the system pre-processes images to anticipate edits, then user interactions are reduced, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary segmentation and preprocessing of the image into semantic areas before user editing commands are issued. By preparing the semantic segmentation maps and data structures in advance, the system enables users to perform edits with minimal interactions, as the heavy computational work of understanding image content has already been completed. This preliminary action reduces the number of user interactions needed while the preprocessing time is acceptable given the one-time nature of the operation.
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.