Joint Feature-Style Latent Editing for Artifact-Reduced Image Generation
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Solution Overview
Problem
Conventional image generating systems using generative neural networks face challenges with high computational costs, inefficiency, and the introduction of unwanted visual artifacts during latent-based editing of digital images, often requiring significant time and resources while compromising accuracy and flexibility.
Innovation Solution
The implementation of a joint feature style (JFS) latent space within generative neural networks for editing digital images, which includes mapping style vectors and feature tensors to a joint feature style space, applying a joint feature style perturbation, and utilizing a locality regularization term to preserve image details and reduce reconstruction error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional latent-based editing methods are used to edit digital images, then editability is improved, but visual artifacts and distortion are introduced
Solution Approach 1:
The patent introduces an intermediary optimization process that operates in latent space between the original image and the edited image. This optimization process uses gradient descent to minimize reconstruction error while applying edits, effectively mediating between the conflicting requirements of editability and artifact reduction. The optimization acts as a buffer that transforms edits smoothly without introducing harsh artifacts.
Solution Approach 2:
The patent modifies parameters in the latent space representation of the image rather than directly manipulating pixel values. By changing latent parameters and then optimizing them through gradient descent, the system achieves edits with fewer visual artifacts. The parameter optimization process adjusts latent vectors to balance edit strength with image fidelity.
2Manufacturing precision
If conventional latent-based editing methods are used to preserve image details, then image fidelity is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary mapping of the input image to latent space parameters before the actual editing process. This preliminary action prepares the data in a form that enables faster subsequent optimization and editing operations. By pre-processing the image into latent representation, the system reduces the computational burden during the actual editing and optimization phases.
Solution Approach 2:
The patent applies optimization only to the necessary latent parameters rather than processing the entire image data. By focusing computational resources on optimizing only the relevant latent vectors that affect the edit, the system achieves good image fidelity with reduced processing time compared to full-image optimization methods.
3Adaptability or versatility
If conventional latent-based editing methods are used to maintain editability, then flexibility is improved, but computational costs and resource utilization increase
Solution Approach 1:
The patent segments the editing process into distinct stages: latent space mapping, perturbation application, and optimization. Each stage operates on specific data structures and parameters independently. This segmentation allows the system to maintain flexibility in each stage while optimizing computational resource usage by processing only the necessary data at each step rather than handling full images throughout.
Solution Approach 2:
The patent replaces traditional mechanical image editing operations with neural network-based latent space operations. Instead of manipulating pixel data through conventional image processing algorithms, the system uses learned latent representations and gradient-based optimization, which are computationally more efficient while maintaining or improving editing flexibility.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for latent-based editing of digital images using a generative neural network. In particular, in one or more embodiments, the disclosed systems perform latent-based editing of a digital image by mapping a feature tensor and a set of style vectors for the digital image into a joint feature style space. In one or more implementations, the disclosed systems apply a joint feature style perturbation and/or modification vectors within the joint feature style space to determine modified style vectors and a modified feature tensor. Moreover, in one or more embodiments the disclosed systems generate a modified digital image utilizing a generative neural network from the modified style vectors and the modified feature tensor.