Semantic Scene Graphs for Digital Image Editing
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring users to interact with individual pixels and perform multiple steps to edit digital images, which can be cumbersome and require significant user knowledge.
Innovation Solution
A scene-based image editing system that utilizes machine learning models to process digital images, pre-processing them to identify objects, generate object masks, and create content fills, allowing users to edit images by interacting with semantic areas rather than pixels, reducing the need for extensive user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image editing systems require users to interact with individual pixels, then editing precision can be achieved, but ease of operation deteriorates significantly
Solution Approach 1:
The patent segments the image into distinct object regions using machine learning models that identify and separate different objects. Instead of requiring users to manually select individual pixels, the system automatically segments objects into coherent regions, allowing users to edit entire objects with simple interactions. This resolves the contradiction by providing both precision (through accurate object segmentation) and ease of operation (through automated region identification).
Solution Approach 2:
The patent introduces an intermediary layer between the user and the pixel data - a machine learning-based object segmentation system. This intermediary automatically processes the raw pixel data and presents simplified object-level selections to the user, eliminating the need for direct pixel manipulation while maintaining editing precision through accurate object boundary detection.
2Adaptability or versatility
If conventional image editing systems provide basic editing tools, then device complexity remains low, but adaptability deteriorates as the system cannot understand semantic relationships
Solution Approach 1:
The patent enables the system to perform self-service by automatically analyzing image content, identifying objects, and understanding their semantic relationships without requiring complex user instructions. The machine learning models autonomously segment objects and determine their relationships, providing high adaptability while keeping the user interface simple. This resolves the contradiction by allowing advanced semantic understanding without exposing the underlying complexity to users.
3Manufacturing precision
If image editing requires multiple manual steps, then manufacturing precision of edits can be controlled, but productivity deteriorates due to cumulative user interactions
Solution Approach 1:
The patent performs preliminary actions by automatically segmenting objects and preparing edit-ready regions before the user initiates editing. The machine learning models pre-process the image to identify objects, determine their boundaries, and establish semantic relationships in advance. This allows users to perform edits in fewer steps while maintaining precision, as the complex preparatory work has already been completed automatically.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer-readable media that generate and implement semantic scene graph for digital image editing. For instance, in some embodiments, the disclosed systems receive a digital image from a client device. The disclosed systems determine, utilizing one or more neural networks, characteristics of the digital image by determining a plurality of objects portrayed in the digital image and a plurality of relationships associated with the plurality of objects. Further, the disclosed systems generate a semantic scene graph for the digital image based on its characteristics. For example, in some cases, the disclosed systems generate a structure of nodes and edges representing the characteristics of the digital image utilizing an image analysis graph and assign behaviors to the plurality of objects based on the plurality of relationships using a behavioral policy graph. The disclosed systems modify the digital image using the semantic scene graph.