Realistic Personal Style Transfer With Controllable GAN Layers
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Solution Overview
Problem
Existing communication technologies fail to accurately and intelligently capture a user's personal style, such as hair or makeup, and apply it realistically to other images, leading to users feeling unpresentable and avoiding video calls.
Innovation Solution
A modified Generative Adversarial Network (GAN) is used to learn a user's personal style from images and apply it to other images, such as video feeds, using multiple discriminators to ensure realism and user control over the style application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing machine learning models are used for style transfer, then style transfer functionality is provided, but the models are inaccurate and destructive with no user control
Solution Approach 1:
The style transfer process is segmented into separate controllable layers. The system identifies distinct style elements (makeup, hair, clothing) and applies them as separate adjustable layers, allowing users to control each layer independently rather than dealing with a monolithic destructive transformation.
Solution Approach 2:
The style transfer system transitions from static pre-applied filters to dynamic, adjustable style layers. Users can dynamically modify the intensity and application of different style elements in real-time, making the system adaptable to individual preferences and scenarios.
2Adaptability or versatility
If predetermined standardized filters are applied to user images, then filtering functionality is provided, but the filters do not capture personal style accurately
Solution Approach 1:
The system automatically captures and applies a user's personal style without manual intervention. By analyzing images of the user and automatically identifying their unique style characteristics, the system serves itself in capturing personal style data and applying it appropriately, eliminating the need for users to manually define their style preferences.
Solution Approach 2:
The system continuously refines its understanding of personal style by analyzing multiple images and providing feedback loops where the captured style information is iteratively improved. This feedback mechanism enables increasing accuracy in personal style capture over time as the system learns more about the user's preferences and characteristics.
3Productivity
If users do not manually prepare their appearance, then time is saved, but users appear unpresentable in video calls
Solution Approach 1:
The system performs preliminary style capture and application automatically before video calls occur. By pre-analyzing images and pre-applying the user's personal style to video feeds, the system eliminates the need for users to manually prepare their appearance in real-time, ensuring they appear presentable without sacrificing time efficiency.
Data Source
AI summary
Computerized systems are provided for applying data indicative of a personal style to a feature of a user represented in one or more images based on determining or estimating the personal style. In operation, embodiments can receive a first image of a first user that indicates the personal style of the first user. The first image can then be fed to one or more machine learning models in order to learn and capture the personal style of the first user. Subsequently, some embodiments capture the first user in another image or set of images. Some embodiments can then detect one or more features of the first user in these other images and based on the determining of the user's personal style in the first image, can apply data indicative of the personal style of the first user to the one or more features of the user in these other images.