Geometry-Lighting-Aware Object Recommendation for Realistic Composites
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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 the compatibility of foreground objects with background images, often requiring tedious user interactions and lacking consideration of lighting and geometry aspects.
Innovation Solution
A geometry-lighting-aware neural network is developed to learn model parameters for foreground object retrieval, incorporating alternating parameter updates and contrastive approaches, enabling flexible and efficient user interfaces for image composition, even without query bounding boxes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image composition systems use inflexible models, then the system complexity is reduced, but the accuracy of determining foreground object compatibility with background images deteriorates
Solution Approach 1:
The patent applies parameter changes by introducing geometry-aware and lighting-aware parameters into the neural network model. The model learns parameters that capture geometric relationships and lighting conditions between foreground objects and background images, enabling accurate compatibility determination. This transforms the model from a simple semantic matching system to one that incorporates multiple dimensional parameters including spatial geometry and photometric lighting characteristics.
Solution Approach 2:
The patent uses composite materials by combining multiple feature representations (semantic features, geometric features, lighting features) into a unified neural network model. The model integrates different types of information about foreground objects and background images into a composite representation that can simultaneously evaluate compatibility across multiple dimensions, achieving high accuracy without requiring separate simple models for each feature type.
2Ease of operation
If conventional systems use tedious workflows, then the ease of operation is reduced, but the flexibility and adaptability of the system deteriorates
Solution Approach 1:
The patent applies self-service by implementing an automated object retrieval system that uses a geometry-lighting-aware neural network to automatically select compatible foreground objects for image composition. The system performs self-contained operations including automatic feature extraction, compatibility evaluation, and object recommendation without requiring manual user intervention for these tasks. Users simply provide background images and optional query bounding boxes, and the system autonomously completes the object search and composition process.
3Manufacturing precision
If the neural network incorporates geometry and lighting awareness, then the manufacturing precision of compatibility determination is improved, but the training complexity and computational resources required increase
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing geometric and lighting features during the training phase. The neural network is pre-trained on large datasets with annotated geometric and lighting information, allowing the model to capture complex relationships between foreground objects and background images before actual inference. This pre-training process establishes the foundation for accurate compatibility determination while managing the complexity of training through systematic data preparation and model initialization.
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.