Style Transfer Neural Network for Spatially Consistent Image Synthesis
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Solution Overview
Problem
Existing photorealistic image stylization methods generate spatially inconsistent stylizations with noticeable artifacts, and training deep neural networks using computer-generated images results in performance gaps compared to real images due to covariate alignment issues.
Innovation Solution
A style transfer neural network approach that uses a photorealistic image stylization technique involving a whitening and coloring transform (WCT) and a manifold ranking algorithm for spatially consistent stylization, aligning covariates between synthetic and real images through iterative processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional photorealistic image stylization methods are used, then stylization speed is improved, but spatial consistency and artifact reduction deteriorate
Solution Approach 1:
The patent segments the stylization process into distinct computational stages: covariance matrix calculation, feature transformation, and image synthesis. This segmentation allows parallel processing of different image regions and features, improving computational efficiency while maintaining spatial consistency through structured feature space operations.
Solution Approach 2:
The patent transforms the stylization problem from pixel-space operations to feature-space operations using covariance matrices and eigenvalue decomposition. By operating in the transformed feature dimension rather than direct pixel dimension, the method achieves both computational efficiency and spatial consistency, as the feature space captures structural relationships that preserve spatial coherence.
2Productivity
If computer-generated images are used for training deep neural networks, then data generation efficiency is improved, but recognition performance deteriorates due to covariate misalignment
Solution Approach 1:
The patent implements an iterative feedback mechanism where the stylization process is repeatedly applied to synthetic images, with each iteration using the previous results to refine covariate alignment. The feedback loop continuously adjusts the transformation parameters to minimize distributional gaps between synthetic and real image domains, progressively improving recognition performance while maintaining efficient synthetic data generation.
Solution Approach 2:
The patent dynamically adjusts transformation parameters including covariance matrix eigenvalues, eigenvectors, and transformation coefficients based on the measured covariate alignment between synthetic and real images. By changing these parameters iteratively to match statistical properties of real images, the method preserves the efficiency of synthetic data generation while achieving the covariate alignment necessary for high recognition performance.
3Device complexity
If simple stylization methods are used, then computational complexity is reduced, but artifact generation increases
Solution Approach 1:
The patent introduces covariance matrices and feature transformation intermediaries that mediate between the source and target image domains. These intermediary mathematical structures capture the essential statistical relationships without requiring complex pixel-wise operations, thereby reducing computational complexity while preventing artifacts by ensuring smooth, structurally consistent transformations through the feature space.
Data Source
AI summary
A style transfer neural network may be used to generate stylized synthetic images, where real images provide the style (e.g., seasons, weather, lighting) for transfer to synthetic images. The stylized synthetic images may then be used to train a recognition neural network. In turn, the trained neural network may be used to predict semantic labels for the real images, providing recognition data for the real images. Finally, the real training dataset (real images and predicted recognition data) and the synthetic training dataset are used by the style transfer neural network to generate stylized synthetic images. The training of the neural network, prediction of recognition data for the real images, and stylizing of the synthetic images may be repeated for a number of iterations. The stylization operation more closely aligns a covariate of the synthetic images to the covariate of the real images, improving accuracy of the recognition neural network.