Machine-Learned Virtual Try-On Avatars for Single-Photo Styling
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Solution Overview
Problem
Existing virtual try-on systems require multiple user perspectives, are tedious, and lack the ability to realistically drape clothing on different poses, and do not allow users to select styling options like sleeves rolled up or tucked in.
Innovation Solution
A system using computer vision and machine learning techniques for virtual try-on and styling, including selecting a garment, generating a semantic segmentation, extracting the garment, determining correspondence, performing garment warping and alignment, and overlaying it on a user's avatar, with machine learning models to infer preferred styles and display the outfit in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple user perspectives are required for virtual try-on, then the accuracy of clothing fit is improved, but the ease of operation deteriorates
Solution Approach 1:
The system performs preliminary action by capturing user body measurements and creating a digital avatar model in advance. This pre-established digital twin allows the clothing to be virtually tried on using machine learning-based warping and rendering, eliminating the need for multiple perspective captures while maintaining fit accuracy.
2Ease of operation
If existing systems attempt to enable virtual try-on with limited inputs, then the ease of operation is improved, but the manufacturing precision of clothing drape deteriorates
Solution Approach 1:
The patent replaces traditional mechanical photogrammetry systems with machine learning-based image warping and generative models. The system uses neural networks to predict clothing deformation and drape based on a single user photo, substituting complex mechanical capture systems with AI-driven virtual try-on that maintains manufacturing precision.
3Reliability
If users are required to provide multiple perspectives, then the reliability of virtual try-on is improved, but the loss of time increases
Solution Approach 1:
The system creates a digital copy (avatar) of the user's body from a single photograph. This digital twin serves as a reliable foundation for virtual try-on, eliminating the need for multiple perspective captures. The copying approach maintains reliability while significantly reducing the time required compared to traditional multi-perspective methods.
4Adaptability or versatility
If existing systems lack styling options, then the device complexity is reduced, but the adaptability deteriorates
Solution Approach 1:
The system implements dynamic styling options that allow users to adjust clothing attributes such as sleeve length, tuck style, and fit preferences in real-time. The machine learning model dynamically adapts the virtual try-on rendering based on user-selected styling parameters, providing high adaptability while managing complexity through efficient image generation techniques.
Data Source
AI summary
Disclosed are example implementations of systems and methods for virtual try-on of articles of clothing. An example method of virtual try-on of articles of clothing includes ingesting information specifying articles of clothing using a first machine learning model; retrieving a stored outfit, the stored outfit comprising at least one garment; inferring a style for the stored outfit preferred by the user with a second machine learning model; generating a shopper avatar wearing the stored outfit with the inferred style based on a physique of a user; and providing the shopper avatar wearing the stored outfit with the inferred style for display to the user in a user interface.