Automatic Object Re-colorization via Color Transformer Network
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Solution Overview
Problem
Current color manipulation techniques in image editing require significant user input and are limited by the need for manual selection of pixels or specific training data, making them inefficient for automatic object re-colorization, especially in high-resolution images.
Innovation Solution
A fully automatic color transformer network (FACT) based on a generative adversarial network (GAN) that automatically recolors objects in images using a simple command, extracting masks and applying target colors without manual edits, trained using adversarial and cycle losses to ensure accurate color transformation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional palette-based recoloring techniques are used, then high-resolution images can be processed, but significant user input is required to select pixels that should change color
Solution Approach 1:
The system performs automatic object segmentation and mask generation without user intervention. The GAN-based model automatically identifies objects, creates segmentation masks, and applies color transformations based on natural language commands, eliminating the need for manual pixel selection while maintaining high color transformation accuracy
Solution Approach 2:
The model integrates multiple functions into a single system: object detection, segmentation mask generation, color space transformation, and color palette mapping. This multi-functional approach allows the system to handle various recoloring tasks automatically without requiring separate user actions for each step
2Ease of operation
If deep learning-based recoloring techniques are used, then only the color of interest can be changed, but training data is required and only low-resolution images can be processed
Solution Approach 1:
The system transforms the problem from requiring training data to using unsupervised learning with parameter-based color mapping. By changing the learning paradigm from supervised (requiring training images) to unsupervised (using color space transformations and palette mapping), the model can process high-resolution images without needing extensive training datasets
Solution Approach 2:
The model operates in color space dimensions (HSV/LAB) rather than directly in pixel space. By transforming the recoloring problem into color space manipulation with automatic palette extraction and mapping, the system achieves selective color change capability without requiring training data, effectively adding a dimensional transformation layer to the processing pipeline
3Manufacturing precision
If manual pixel selection is required for recoloring, then color transformation can be controlled, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary object segmentation and mask generation automatically before color transformation. By pre-processing the image to identify objects and create segmentation masks without user intervention, the model prepares the groundwork for controlled color changes while eliminating the time-consuming manual pixel selection step
Solution Approach 2:
The system introduces an intermediary segmentation mask between the original image and the color transformation process. This mask acts as a mediator that automatically defines which regions should be recolored, maintaining precise color change control while eliminating the need for manual pixel selection and significantly improving processing efficiency
Data Source
AI summary
Embodiments are disclosed for automatic object re-colorization in images. In some embodiments, a method of automatic object re-colorization includes receiving a request to recolor an object in an image, the request including an object identifier and a color identifier, identifying an object in the image associated with the object identifier, generating a mask corresponding to the object in the image, providing the image, the mask, and the color identifier to a color transformer network, the color transformer network trained to recolor objects in input images, and generating, by the color transformer network, a recolored image, wherein the object in the recolored image has been recolored to a color corresponding to the color identifier.