Image Segmentation Model for Universal Hair Dyeing Effects
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Solution Overview
Problem
Existing image processing technologies struggle to accurately and universally apply hair dyeing effects due to variations in hairstyles and hair dyeing effects among users, leading to inaccuracy and the need for numerous models to be trained for different effects.
Innovation Solution
A method and apparatus for image processing that collect an image with a target object, segment it using an image segmentation model to identify target render regions, and apply a target effect based on these regions and an effect parameter, thereby creating a target image with the added effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If numerous models are trained for different hair dyeing effects, then the diversity of effects is improved, but the device complexity and training time increase significantly
Solution Approach 1:
The patent implements a universal image processing model that can handle multiple hair dyeing effects through a unified architecture. The model accepts effect parameters as input and generates different dyeing effects without requiring separate models for each effect type, thereby achieving multi-functionality and reducing the number of models needed.
Solution Approach 2:
The patent utilizes effect parameters to control the output of the universal model. By changing the effect parameters while keeping the model structure fixed, the system can generate diverse hair dyeing effects, thus achieving versatility without increasing model complexity.
2Adaptability or versatility
If numerous models are trained for different hair dyeing effects, then the diversity of effects is improved, but the training time and computational resources increase
Solution Approach 1:
The universal model is trained once to handle multiple hair dyeing effects, eliminating the need for repeated training of separate models for each effect. This significantly reduces training time and computational resource consumption while maintaining effect diversity.
Solution Approach 2:
The model achieves different effects through parameter adjustments rather than retraining, which saves substantial training time and computational resources compared to training multiple specialized models.
3Device complexity
If a single universal model is used for different users, then the device complexity is reduced, but the accuracy for individual users decreases
Solution Approach 1:
The patent segments the image into different regions (hair region, skin region, etc.) using an image segmentation model. This allows the universal model to apply effects accurately to specific regions for each user, maintaining high precision while using a single universal model.
Solution Approach 2:
The patent applies different processing to different regions of the image. The image segmentation model identifies specific regions, and the universal model applies appropriate effects to each region based on its characteristics, thereby achieving user-specific accuracy with a universal model.
Data Source
AI summary
The disclosure provides a method and apparatus of image processing, an electronic device, and a storage medium. The method of image processing includes: collecting, in response to an effect addition instruction, an image to be processed including a target object; segmenting the image to be processed based on an image segmentation model to obtain at least two target render regions corresponding to the image to be processed; and obtaining, based on the at least two target render regions and an effect parameter, a target image including the target object with an added target effect.