Neural Scene Graph Compositing for Non-Destructive Image Editing
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Solution Overview
Problem
Existing image editing technologies lack a coherent and non-destructive workflow that effectively integrates generative models with traditional editing tools, leading to inconsistent and unpredictable image modifications.
Innovation Solution
A neural compositing method that utilizes a machine learning model with a neural scene graph to maintain semantic consistency and iterative, non-destructive image modification by integrating generative models like VAE, GAN, and diffusion models, allowing users to add or modify content while preserving image continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional image editing tools are used separately from generative models, then editing control and precision are maintained, but workflow coherence and integration are poor
Solution Approach 1:
The patent combines traditional image editing tools with generative models into a unified system. The scene graph serves as a common data structure that both traditional editing operations and generative model outputs can interact with, merging previously separate workflows into a coherent integrated system where users can switch between deterministic editing and probabilistic generation seamlessly.
Solution Approach 2:
The scene graph acts as a universal interface that handles both traditional editing operations and generative model outputs. It can represent various types of image elements (objects, regions, attributes) and support multiple operations (editing, generation, modification) through a single unified data structure, making the system versatile across different editing paradigms.
2Adaptability or versatility
If generative models are integrated without a unified workflow, then creative flexibility increases, but editing consistency and predictability decrease
Solution Approach 1:
The scene graph provides a feedback mechanism where generative model outputs are represented as editable graph elements that can be inspected, modified, and adjusted. Users can review generated content within the scene graph structure and make iterative adjustments, providing feedback loops that ensure consistency while maintaining creative flexibility. The scene graph state serves as feedback between generation and editing stages.
Solution Approach 2:
The system performs preliminary organization of generative model outputs into scene graph structures before final image generation. By pre-organizing generated elements into a coherent scene graph with defined relationships and attributes, the system ensures consistency is established early in the workflow, allowing subsequent editing operations to proceed predictably.
3Adaptability or versatility
If diffusion models add noise to destroy input data, then generative capability is achieved, but original image information is lost
Solution Approach 1:
The scene graph segments the image into discrete editable elements (objects, regions, attributes) before generative operations. This segmentation allows selective application of generative models to specific graph elements rather than the entire image, preserving information in unmodified graph elements while enabling generation where needed. The scene graph structure maintains organization and references to original content throughout the generative process.
Solution Approach 2:
The system selectively discards only the portions of original image data that are explicitly replaced by generative operations, while recovering and preserving all other original content through the scene graph structure. The scene graph maintains references to original image regions and attributes, allowing recovery of unmodified content and ensuring that generative operations do not inadvertently lose original information.
Data Source
AI summary
One or more aspects of the method, apparatus, and non-transitory computer readable medium include obtaining an original image, a scene graph describing elements of the original image, and a description of a modification to the original image. The one or more aspects further include updating the scene graph based on the description of the modification. The one or more aspects further include generating a modified image using an image generation neural network based on the updated scene graph, wherein the modified image incorporates content based on the original image and the description of the modification.