Geometry-Lighting-Aware Foreground Retrieval for Realistic Composition
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Solution Overview
Problem
Conventional image composition systems fail to provide realistic compositions due to inflexible models that do not accurately determine compatibility of foreground objects with background images, often requiring tedious and inefficient user interactions.
Innovation Solution
Implementing a geometry-lighting-aware neural network that learns model parameters through an alternating parameter-update strategy and contrastive approach, allowing for flexible object retrieval and composition, even without a query bounding box, and providing an intuitive user interface for efficient image composition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional inflexible models are used for object retrieval, then the system structure is simple, but the accuracy of determining compatibility between foreground objects and background images deteriorates
Solution Approach 1:
The patent implements a dynamic neural network model that alternates between updating background encoder parameters and foreground-object/illumination parameters. This dynamic parameter update mechanism allows the system to adaptively learn compatibility relationships, resolving the contradiction between measurement precision and device complexity by making the model flexible yet structured.
Solution Approach 2:
The system changes parameters by learning background encoder parameters and foreground-object parameters separately through alternating updates. This parameter separation and iterative optimization approach enables accurate compatibility determination while maintaining manageable model complexity through structured learning.
2Productivity
If conventional object retrieval systems are used, then the workflow is simple, but the amount of user interaction required increases
Solution Approach 1:
The system performs self-service by automatically retrieving compatible foreground objects and generating composite images without requiring extensive user interaction. The neural network model autonomously determines compatibility and executes the composition workflow, significantly improving productivity while reducing operational complexity.
3Reliability
If conventional image composition systems are used, then the processing speed is fast, but the realism of composite images deteriorates
Solution Approach 1:
The patent segments the image composition problem into separate components: background encoding, foreground object representation, and illumination modeling. By segmenting the compatibility determination into these distinct elements and learning them separately through alternating parameter updates, the system achieves realistic composite images while maintaining computational efficiency.
Solution Approach 2:
The system combines multiple learned representations (background features, foreground object features, illumination parameters) into a composite compatibility assessment. This composite approach integrates multiple factors that contribute to realism, resolving the contradiction between image quality and model complexity.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes artificial intelligence to learn to recommend foreground object images for use in generating composite images based on geometry and/or lighting features. For instance, in one or more embodiments, the disclosed systems transform a foreground object image corresponding to a background image using at least one of a geometry transformation or a lighting transformation. The disclosed systems further generating predicted embeddings for the background image, the foreground object image, and the transformed foreground object image within a geometry-lighting-sensitive embedding space utilizing a geometry-lighting-aware neural network. Using a loss determined from the predicted embeddings, the disclosed systems update parameters of the geometry-lighting-aware neural network. The disclosed systems further provide a variety of efficient user interfaces for generating composite digital images.