Image Stylization Pipeline for Protected Skin Tone Features
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Solution Overview
Problem
Existing image stylization systems fail to accurately preserve and enhance specific features of a source image, such as skin tone, during the transformation to a target domain style, leading to inaccurate representation of diverse user groups.
Innovation Solution
An image stylization system that employs a five-staged pipeline, including an image collector, unsupervised machine learning model, dataset adjustment, image transformation, and supervised image translation, to preserve and enhance selected features like skin tone during the transformation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image stylization transformation is applied to convert source image to target domain style, then the stylized representation is achieved, but the accuracy of protected features (such as skin tone) deteriorates
Solution Approach 1:
The patent segments the image processing into distinct stages: feature detection, feature protection, and stylization transformation. By separating the protected feature extraction from the stylization process, the system maintains feature accuracy while achieving stylization. The feature protection module specifically identifies and preserves skin tone and other protected features independently from the overall stylization transformation.
Solution Approach 2:
The patent applies local quality by treating different regions of the image differently during stylization. Protected features such as skin tone are subjected to different processing than other image regions. The system applies feature-preserving transformations specifically to protected areas while allowing full stylization in non-protected areas, thereby maintaining local feature accuracy while achieving global stylization.
2Productivity
If existing stylization systems are used to transform images to target domain style, then the transformation efficiency is maintained, but the representation accuracy of diverse user groups deteriorates
Solution Approach 1:
The patent implements preliminary action by detecting and protecting features before the stylization transformation occurs. The feature detection and protection modules operate in advance of the main stylization process, identifying skin tone and other protected features that need preservation. This preliminary protection ensures that subsequent stylization operations do not degrade the accuracy of diverse user group representations.
Solution Approach 2:
The system incorporates feedback mechanisms where the protected feature extraction results are fed back into the stylization process to guide the transformation. The feature protection module continuously monitors protected features during stylization and adjusts the transformation parameters to maintain feature accuracy. This feedback loop ensures that representation accuracy is preserved while maintaining transformation efficiency.
Data Source
AI summary
Systems and methods herein describe an image stylization system. The image stylization system accesses a set of images corresponding to a target domain style, generates a set of paired images using a first machine learning model, analyze the generated set of paired images using a second machine learning model trained to analyze the generated set of paired images based on a plurality of protected feature criteria, determines a set of image transformations for the generated set of pairs, generates a transformed set of paired images by performing the set of image transformations on the set of paired images, and generates stylized images corresponding to the target domain style using a supervised image translation model trained on the transformed set of paired images.