Segmented Image Generation via Multiple Generators
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Solution Overview
Problem
Existing image editing techniques using generative adversarial networks (GANs) face challenges in accurately editing specific features of an image without affecting other parts, due to the high number of constraints imposed by treating the entire image as a single latent code, leading to less realistic and poorly disentangled results.
Innovation Solution
The approach involves segmenting the image into multiple regions using a segmentation mask, with each region being generated separately by a different generator network, allowing for more accurate and precise editing by optimizing individual latent codes for each segment, and then stitching the segments together to form the final output image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire image is represented by a single latent code, then the reconstruction accuracy can be maintained, but the number of constraints increases making code estimation difficult and reducing editing precision
Solution Approach 1:
The patent divides the image into multiple segments, each represented by a separate latent code. This segmentation reduces the constraint complexity for each individual code estimation while maintaining overall reconstruction accuracy through coordinated optimization of multiple codes.
2Ease of operation
If the entire image is represented by a single latent code, then the system structure remains simple, but the disentanglement of visual features is inadequate making editing difficult
Solution Approach 1:
The patent creates multiple generator networks, each responsible for a specific image segment. This enables independent manipulation of different visual features through separate latent codes, improving editing ease while the modular structure manages complexity through specialization.
Solution Approach 2:
Each generator network is specialized for its specific segment, allowing local optimization of editing capabilities for different regions. This local quality approach enables feature disentanglement where each segment can be edited independently with appropriate control.
3Manufacturing precision
If multiple generators are used for different segments, then editing precision and disentanglement improve, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the image processing task into multiple specialized generators, each handling a specific region. This achieves high editing precision for localized features while managing system complexity through modular architecture where each component has a defined scope.
Data Source
AI summary
Embodiments are disclosed for segmented image generation. The method may include receiving an input image and a segmentation mask, projecting, using a differentiable machine learning pipeline, a plurality of segments of the input image into a plurality of latent spaces associated with a plurality of generators to obtain a plurality of projected segments, and compositing the plurality of projected segments into an output image.