Synthetic Image Generation with Localized Semantic Editing
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Solution Overview
Problem
Existing generative adversarial networks (GANs) lack the capability for localized customization in synthetic image generation, failing to effectively utilize disentangled semantic information for spatially-localized semantic editing without requiring complex spatial operations or external supervision.
Innovation Solution
A system that utilizes feature vectors from source and target images to transfer the style of a selected semantic element from the source image to the target image using a generative adversarial network (GAN), such as StyleGAN, without explicit spatial localization, employing clustering techniques like spherical k-means clustering to identify semantically meaningful elements and perform localized editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If GAN is used for semantic image editing with disentanglement, then semantic elements can be manipulated independently, but localized customization capability is lost
Solution Approach 1:
The patent segments the image into distinct semantic elements through disentanglement in latent space, allowing independent manipulation of each semantic element while maintaining the overall image structure. This segmentation enables localized customization by isolating specific semantic regions that can be edited without affecting other parts of the image.
Solution Approach 2:
The patent transforms the problem from spatial domain to latent space dimensionality, where semantic elements are disentangled and can be independently manipulated. By operating in this higher-dimensional latent space rather than directly in pixel space, the system achieves localized customization capability that would be complex to implement through traditional spatial operations.
2Ease of operation
If traditional spatial operations are used for localized editing, then localized customization is achieved, but complex spatial processing is required
Solution Approach 1:
The patent replaces complex mechanical spatial processing operations with a learnable neural network transformation in latent space. Instead of using traditional image processing techniques for localization and editing, the system uses the GAN's latent space representation where semantic elements are naturally disentangled, substituting mechanical spatial operations with a more elegant mathematical transformation.
Solution Approach 2:
The patent changes the parameter space from pixel values to latent space representations, where editing operations become simpler parameter adjustments rather than complex spatial transformations. By modifying latent vectors that control specific semantic elements, the system achieves localized editing with much lower computational complexity compared to operating directly in image space.
3Manufacturing precision
If external supervision is used for semantic editing, then editing accuracy is improved, but the system requires external form of supervision
Solution Approach 1:
The patent enables the GAN system to perform semantic disentanglement and localized editing autonomously without external supervision. The network learns to separate semantic elements in latent space through its own training process, and this self-learned structure enables subsequent localized editing operations without requiring additional external guidance or supervision during the editing phase.
Data Source
AI summary
Embodiments described herein provide a system for generating synthetic images with localized editing. During operation, the system obtains a source image and a target image for image synthesis and selects a semantic element from the source image. The semantic element indicates a semantically meaningful part of an object depicted in the source image. The system then determines the style information associated with the source and target images. Subsequently, the system generates a synthetic image by transferring the style of the semantic element from the source image to the target image based on the feature representations. In this way, the system can facilitate localized editing of the target image.