Real-Time Virtual Apparel Calibration via Neural Network Inferences
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Solution Overview
Problem
Current virtual try-on solutions for 3D clothing lack real-time interaction capabilities, failing to accurately adjust the shape, size, and orientation of virtual clothing to match a user's body contours, resulting in a suboptimal AR experience.
Innovation Solution
A software-based system using AR and AI that allows users to interact with virtual clothing in real-time through a smartphone or tablet, utilizing body measurements, gestures, and pressure sensors to adjust the fit and design of virtual apparel, enabling real-time modification and fitting based on user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If 2D image transfer technology is used for virtual try-on, then the implementation complexity is reduced, but the real-time interaction capability and accuracy of fitting 3D clothing to body contours deteriorates
Solution Approach 1:
The patent replaces traditional 2D image transfer mechanical processes with a neural network-based system that processes 3D body scans and clothing models to generate realistic virtual try-on images in real-time, enabling accurate 3D clothing fitting while maintaining system manageability
Solution Approach 2:
The system changes the fundamental parameters from 2D image coordinates to 3D body surface geometry parameters, allowing the virtual clothing to conform accurately to the user's body contours while enabling real-time interaction through adjustable parameters like pose, lighting, and clothing modifications
2Speed
If traditional virtual try-on methods are used, then the processing speed is faster, but the accuracy of adjusting clothing shape, size, and orientation to match body contours deteriorates
Solution Approach 1:
The system performs preliminary 3D body scanning and measurement before the actual virtual try-on, capturing accurate body contours and characteristics in advance. This preliminary action enables the neural network to quickly generate accurate virtual try-on images without compromising processing speed, as the complex measurement task is completed beforehand
Solution Approach 2:
The patent creates an accurate 3D digital copy of the user's body using scanning technology, which serves as a precise template for rendering virtual clothing. This digital twin approach maintains high accuracy in clothing fit while enabling real-time interactions without repeated complex measurements
3Adaptability or versatility
If interactive adjustments are enabled for real-time customization, then the user satisfaction and fitting accuracy improve, but the computational complexity and processing time increase
Solution Approach 1:
The neural network system automatically adjusts virtual clothing parameters based on user interactions and 3D body data, performing self-service optimizations without requiring complex manual computations. The system handles pose adjustments, lighting changes, and clothing modifications autonomously, reducing the perceived computational complexity for users
Solution Approach 2:
The system implements real-time feedback loops where user interactions with the virtual try-on image trigger automatic recalculations and adjustments. The neural network continuously refines the virtual clothing representation based on feedback from user actions, maintaining adaptability while managing computational complexity through iterative optimization
Data Source
AI summary
An Augmented Reality (AR) and Artificial Intelligence (AI) based interactive virtual try-on solution that facilitates trying on, fitting, and modularizing a virtual apparel in real-time—as if a consumer were wearing the apparel. A user with a mobile device defines retail adjustment operations on the virtual apparel using an AR-based visual interface. The user can interact with the virtual apparel for identifying, defining, and changing the look, fit, and design of the apparel on the user's body. The real-time interaction is with the same virtual apparel. The system defines operations based on user's features, sartorial measurements, intent, gestures, position, pressure values received from a controller operated by the user, and the sensed motion of the user to translate into a set of machine learning inference models that predict a series of states that visually generate the outcome the user anticipates based on the user's interaction with the virtual clothing.