Hair Image Editing With Identity-Preserving Composite Generation
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Solution Overview
Problem
Current methods for editing hair characteristics in digital images often fail to produce photorealistic outputs and do not adequately preserve the identity of the person.
Innovation Solution
A computer-implemented method using a generative neural network to modify hair features in a digital image, combined with face and background areas to create a composite image, while preserving identity through face swapping and inpainting techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If current methods are used to edit hair characteristics in digital images, then the editing process can be completed, but the output images are not photorealistic and the person's identity is not preserved
Solution Approach 1:
The image is divided into distinct regions: face area, hair area, and background area. The system extracts the face area from the original image, generates modified hair separately, and then combines them. This segmentation allows independent optimization of each region to achieve both photorealism and identity preservation.
Solution Approach 2:
A generative neural network acts as an intermediary to create modified hair that bridges the original hair characteristics and the desired hairstyle changes. The network generates photorealistic hair modifications while preserving the person's identity characteristics, serving as a mediator between the original image and the final composite.
2Manufacturing precision
If multiple processing steps are used to achieve photorealistic hair modification and identity preservation, then the quality of output improves, but the processing time increases
Solution Approach 1:
The system performs preliminary extraction of the face area and background area from the original image before generating the modified hair. This preliminary segmentation allows the generative network to focus specifically on hair modification without redundant processing, and enables parallel processing of different image regions to reduce overall processing time.
Solution Approach 2:
The system merges the extracted face area, modified hair area, and background area into a single composite image in one final operation. This combining step integrates all processed elements efficiently, producing the final photorealistic output without requiring multiple separate rendering passes.
Data Source
AI summary
An example computer-implemented image processing method includes receiving a digital image including a face area of a person and a background area, receiving an input parameter for modification of a feature of hair on the person, modifying the digital image based on the input parameter utilizing a generative neural network to create a modified digital image in which the feature of the hair on the person is modified, extracting a hair area from the modified digital image, and the face area and the background area from the digital image, and combining the face area and the background area from the digital image with the hair area from the modified digital image to create a composite modified digital image that includes data representing a modified feature of hair on the person from the modified digital image and data representing the face area and the background area from the digital image.