GAN Image Generation with Semantic and Color Control
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Solution Overview
Problem
Existing image generation models cannot edit colors locally once an image is generated, and colorization techniques lack image generation capabilities, limiting user control over both semantics and color in generated images.
Innovation Solution
A GAN-based drawing board that incorporates a novel color feature embedding technique, allowing users to input semantic labels and color strokes to generate images with both semantic and color control, enabling real-time creation and editing of images by converting user-entered strokes into high-intensity features for conditional image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If image generation models are used to generate images, then images can be created unsupervised or conditioned on user input, but colors cannot be edited locally once the image is generated
Solution Approach 1:
The patent merges image generation capability and colorization capability into a single unified model. The GAN architecture integrates both the generator for image creation and the colorization module for local color editing, allowing users to perform both operations through one system rather than requiring separate models.
Solution Approach 2:
The unified model serves multiple functions: it can generate images from scratch, apply colorization to existing images, and perform local color editing on generated images. This multi-functional approach eliminates the need for separate color editing tools and simplifies the user workflow.
2Adaptability or versatility
If colorization techniques are used to apply colors to images, then colorization can be achieved, but image generation capabilities are lost
Solution Approach 1:
The patent combines the colorization module with the image generation GAN model into a unified architecture. This integration allows the system to perform both image generation and colorization functions without requiring separate independent systems, thereby maintaining adaptability while managing complexity through unified design.
3Manufacturing precision
If semantic labels are obtained based on user input, then semantic control over image content is achieved, but additional user input processing is required
Solution Approach 1:
The model segments the image into different semantic regions based on user-provided labels, allowing precise control over specific areas. The system divides the image processing task into region-specific operations, enabling fine-grained semantic control while managing complexity through localized processing.
4Ease of operation
If color features are embedded as latent vectors in the GAN model, then local color control is enabled, but the model requires additional input processing
Solution Approach 1:
The patent changes the parameter representation of color information by embedding color features as latent vectors within the GAN model. This parameter transformation allows the model to control local colors through learned representations rather than direct pixel manipulation, enabling intuitive color control while managing complexity through parameter optimization.
Data Source
AI summary
A method for image generation and colorization includes displaying a drawing board interface; obtaining semantic labels of an image to be generated based on user input on the drawing board interface, each semantic label indicating a content of a region in the image to be generated; obtaining a color feature of the image to be generated; and automatically generating the image using a generative adversarial network (GAN) model according to the semantic labels and the color feature. The color feature is a latent vector input to the GAN model.