Neural Network Image Editing via Parsing Map Latent Representation
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Solution Overview
Problem
Existing image editing tools face limitations in accurately editing large areas of images with complex shape changes, often introducing unwanted artifacts and failing to retain source image details in attribute-irrelevant regions.
Innovation Solution
A multi-stage approach using generative adversarial networks (GANs) for image attribute editing, separating shape editing from appearance editing, where a parsing map is manipulated to apply edits only to the target region, allowing for more complex shape changes while preserving the original image's details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If direct manipulation of raw RGB pixels is used for image editing, then the editing process is simple, but the accuracy and detail preservation deteriorate
Solution Approach 1:
The patent segments the image editing process into multiple stages: parsing map generation, latent representation editing, and image synthesis. Instead of directly manipulating RGB pixels, the system operates on segmented parsing maps that identify semantic regions, enabling precise control over which areas are edited while preserving others.
Solution Approach 2:
The patent introduces parsing maps and latent representations as intermediary structures between the edit command and the final image. The parsing map serves as a mediator that translates high-level edit parameters into spatially-aware representations, which then guide the synthesis of accurate edited images without direct pixel manipulation.
2Adaptability or versatility
If reconstruction of large image areas is performed for complex shape operations, then the edit versatility improves, but unwanted artifacts and changes in non-target regions increase
Solution Approach 1:
The patent applies local quality by operating on parsing maps that selectively identify and edit only the target region while leaving other regions unchanged. The edited latent representation is then used to generate the final image, ensuring that edits are localized to the intended area without introducing artifacts or spurious changes elsewhere in the image.
3Manufacturing precision
If high-resolution images are processed to maintain detail, then the image quality improves, but the processing time increases
Solution Approach 1:
The patent transforms the image processing from operating directly on pixel space to operating in the latent representation space. By editing latent codes that encode image properties and then synthesizing the final image, the system achieves high-resolution output without the computational burden of processing every pixel at full resolution throughout the entire pipeline.
Data Source
AI summary
The present disclosure describes multi-stage image editing techniques to improve detail and accuracy in edited images. An input image including a target region to be edited and an edit parameter specifying a modification to the target region are received. A parsing map of the input image is generated. A latent representation of the parsing map is generated. An edit is applied to the latent representation of the parsing map based on the edit parameter. The edited latent representation is input to a neural network to generate a modified parsing map including the target region with a shape change according to the edit parameter. Based on the input image and the modified parsing map, a masked image corresponding to the shape change is generated. Based on the masked image, a neural network is used to generate an edited image with the modification to the target region.