Vector Image Colorization via Diffusion Network and Outline Encoder
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Solution Overview
Problem
Existing vector image colorization techniques, both rule-based and machine learning-based, face challenges such as requiring extensive user input, producing inaccurate fills, especially with sparse color strokes, and failing to generate diverse color variants. Additionally, GAN-based methods often struggle with complex sketches and require significant computational resources, making real-time editing infeasible.
Innovation Solution
The proposed solution utilizes a diffusion network and an outline encoder to generate a synthesized image based on an outline image and color hints. This system includes a control network that provides adaptable encoding layers, allowing for accurate colorization of vector images in real-time, even with sparse input color hints, and enables the integration of text descriptions for further guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rule-based computer vision techniques are used for colorization, then the process can be automated to some extent, but the accuracy of color fills deteriorates especially with sparse color strokes
Solution Approach 1:
The patent introduces color hints as intermediary elements that users can provide to guide the colorization process. These color hints act as mediators between the automated system and the desired output, allowing the system to accurately propagate colors to regions without direct user input while maintaining overall color accuracy and consistency with user intent.
2Adaptability or versatility
If GAN-based methods are used for colorization, then diverse color variants can be generated, but the computational resources required and time consumption increase significantly
Solution Approach 1:
The patent performs preliminary color propagation to generate a complete colorization based on provided color hints before any variant generation occurs. This preliminary action establishes a solid color foundation that can then be efficiently varied through simpler transformations, avoiding the need for computationally expensive re-training or extensive sampling that would otherwise be required to generate diverse variants.
Solution Approach 2:
The patent separates the colorization process into distinct stages: first propagating colors to fill regions based on hints, then generating variants through controlled modifications. This segmentation allows each stage to be optimized independently, with the variant generation stage requiring significantly fewer computational resources than generating colorizations from scratch.
3Manufacturing precision
If extensive user input is required for colorization, then accuracy can be improved, but the ease of operation deteriorates
Solution Approach 1:
The patent implements partial action by allowing colorization to proceed accurately with only sparse color hints provided by the user. The system propagates these limited hints throughout the image using learned color relationships and context, achieving accurate results without requiring complete user specification of all color regions. This partial input approach maintains accuracy while dramatically improving ease of operation.
Data Source
AI summary
Embodiments for colorizing images, including pixel-format images and vector-format images, include obtaining input data including an outline image and a color hint. The color hint includes a colored portion corresponding to a region of the outline image. Then, embodiments process the input data to obtain control guidance for an image generator using an outline encoder. Embodiments generate a synthesized image based on the control guidance using an image generator. The synthesized image depicts an object having a shape based on the outline image and a color based on the color hint. In some cases, embodiments also transfer the colors from the synthesized image to a base vector image to produce a colorized vector image.