Image Vectorization Fidelity Control for Editable Vector Output
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Solution Overview
Problem
Existing image generation models produce non-vectorizable, highly detailed, and photo-realistic images that are difficult to convert efficiently into vector format, often resulting in excessive paths and anchor points, compromising editability and computational demands.
Innovation Solution
A method to control the fidelity and detail of vector images by adjusting noise levels based on input parameters, using a generative diffusion model with style embeddings and vectorization components to generate images with precise semantic integrity and editable vector format.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If image generation models produce highly detailed photo-realistic images, then image quality and realism are improved, but the images become difficult to convert to vector format with excessive paths and anchor points
Solution Approach 1:
The patent applies preliminary action by introducing a vectorization guidance module before the image generation process completes. This module provides early guidance to the diffusion model to steer image generation toward vectorizable characteristics (solid colors, clear boundaries, minimal gradients) before the detailed photo-realistic rendering occurs, preventing the creation of inherently non-vectorizable features rather than attempting to convert them afterward
Solution Approach 2:
The patent applies local quality by differentiating the generation process into regions with different requirements. The vectorization guidance module identifies and emphasizes areas that should have vectorizable properties (logos, icons, geometric shapes) while allowing other areas to maintain photo-realistic detail. This selective approach ensures critical elements are vectorizable without sacrificing overall image quality
2Manufacturing precision
If image generation models produce highly detailed images, then image quality is improved, but computational demands and processing time increase
Solution Approach 1:
The vectorization guidance module performs preliminary action by establishing vectorizable structures early in the generation process. By guiding the diffusion model to create solid color regions and clear boundaries from the outset, the system reduces the computational burden of subsequent vectorization steps, avoiding the need for intensive processing to convert complex gradients and fuzzy edges into vector format
3Manufacturing precision
If image generation models produce photo-realistic images with gradients and fuzzy edges, then image quality is improved, but the images lack clearly defined shapes and boundaries needed for efficient vectorization
Solution Approach 1:
The patent applies local quality by implementing region-aware generation guidance. The vectorization guidance module identifies specific regions (such as logos, icons, and geometric elements) and applies targeted guidance to ensure these areas have solid colors, clear boundaries, and minimal gradients. Other regions can maintain photo-realistic quality with gradients and textures, achieving a balance between overall image quality and vectorization efficiency for critical elements
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system for modulating the level of fidelity to an input image include obtaining an input image and a fidelity parameter. The input image depicts an entity, and the fidelity parameter indicates a level of fidelity, i.e., faithfulness, to the input image. Embodiments then add noise to the input image based on the fidelity parameter to obtain an intermediate noise image. Subsequently, embodiments generate a synthetic image based on the intermediate noise image using an image generation model. The synthetic image includes a vectorizable depiction of the entity and has the level of fidelity to the input image indicated by the fidelity parameter. The vectorizable depiction is more suitable for conversion to vector format, as the resulting vector image will have a reduced number of paths and shapes.