Latent Space Image Editing for Continuous Multi-Attribute Control
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Solution Overview
Problem
Existing image editing tools struggle with accurately editing high-resolution images, introducing unwanted artifacts, and lack control over continuous and multiple attribute edits, particularly in machine learning-based solutions like Generative Adversarial Networks (GANs).
Innovation Solution
Utilizing a non-linear latent filter neural network trained to apply targeted attribute changes directly to a latent space representation of an image, without relying on attribute prediction or shape models during inference, enabling continuous and multiple attribute editing with improved accuracy and reduced computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Generative Adversarial Networks (GANs) are used to generate realistic edited images, then image realism is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent segments the image editing task into two distinct stages: (1) a lightweight network performs initial editing operations on the input image, and (2) a GAN-based network subsequently refines the result to enhance realism. This segmentation allows the computationally expensive GAN to be applied only to specific refinement tasks rather than the entire editing pipeline, reducing overall computational cost while maintaining image realism.
Solution Approach 2:
The patent applies preliminary editing operations using a lightweight network before invoking the GAN-based refinement stage. This preliminary action prepares the image in advance, performing basic attribute modifications that reduce the burden on the subsequent GAN processing, thereby lowering computational cost while preserving the ability to generate realistic final outputs.
2Adaptability or versatility
If existing machine learning techniques are used for image editing, then attribute modification capability is improved, but control over continuous and multiple attributes deteriorates
Solution Approach 1:
The patent introduces an intermediary latent space representation that bridges the input image and the final edited output. This latent space serves as a mediator where continuous attribute adjustments can be made in a controlled manner before being transformed into the final image domain, enabling smooth continuous control over multiple attributes simultaneously while maintaining versatility in modification capabilities.
Solution Approach 2:
The patent transforms the image editing problem from direct pixel-space manipulation into latent space operations, adding an intermediate dimensional layer. This dimensionality change allows for more intuitive and continuous control over multiple attributes by operating in the latent space where attribute relationships are more separable and controllable, then projecting the changes back to the image domain.
3Manufacturing precision
If high-resolution images are edited using existing tools, then image quality is improved, but processing time and computational resources increase excessively
Solution Approach 1:
The patent segments the high-resolution image processing task by applying the lightweight editing network first to perform coarse edits, then using the GAN-based refinement network only on specific regions or at lower resolutions before upscaling. This segmentation strategy maintains high image quality in the final output while significantly reducing the computational burden and processing time compared to applying full GAN processing to the entire high-resolution image.
Solution Approach 2:
The patent applies partial GAN-based refinement rather than full processing to the entire high-resolution image. By using the lightweight network for the majority of editing operations and reserving GAN refinement for specific critical regions or attributes, the system achieves high image quality where needed while minimizing overall processing time and computational resource consumption.
Data Source
AI summary
Systems and methods use a non-linear latent filter neural network for editing an image. An image editing system trains a first neural network by minimizing a loss based upon a predicted attribute value for a target attribute in a training image. The image editing system obtains a latent space representation of an input image to be edited and a target attribute value for the target attribute in the input image. The image editing system provides the latent space representation and the target attribute value as input to the trained first neural network for modifying the target attribute in the input image to generate a modified latent space representation of the input image. The image editing system provides the modified latent space representation as input to a second neural network to generate an output image with a modification to the target attribute corresponding to the target attribute value.