Deep Learning Virtual Try-On System for Multi-Garment Synthesis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current clothing virtual try-on services using deep learning technologies often produce blurred, distorted, and artificially appearing final fitting images, and are limited to fitting single pieces of clothing, failing to provide a realistic experience for users trying to determine whether multiple clothes fit well.
Innovation Solution
A deep-learning-based method and system that generates a virtual fitting image by transforming the shape of multiple clothes objects to correspond to a model object, using a geometric matching module and virtual fitting module to synthesize a natural-looking fitting image, even with limited training data and diverse backgrounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional deep learning methods are used for virtual try-on, then the processing speed is improved, but the image quality becomes blurred and distorted with artificial appearance
Solution Approach 1:
The patent segments the virtual try-on process into distinct modules: a geometric matching module that performs precise geometric transformations to align clothing with body contours, and a virtual fitting module that handles image synthesis. This segmentation allows each module to specialize in its function, with the geometric module ensuring shape accuracy and the fitting module ensuring visual realism, thereby resolving the contradiction between processing speed and image quality.
Solution Approach 2:
The patent introduces an intermediate geometric transformation step that serves as a mediator between the input clothing image and the final synthesized output. This intermediate step performs precise geometric adjustments (scaling, rotating, warping) to match body contours before the final synthesis, ensuring that the clothing maintains proper shape and orientation while being integrated into the final image, thus preventing distortion and artificial appearance.
2Device complexity
If conventional systems focus on single piece of clothing fitting, then the system complexity is reduced, but the user need for multiple clothes assessment is not satisfied
Solution Approach 1:
The patent implements a universal virtual try-on system that can handle multiple types of clothing (tops, bottoms, dresses, etc.) and multiple body types through a single integrated platform. The geometric matching module and virtual fitting module are designed with general algorithms that adapt to different clothing categories and body shapes, allowing users to assess complete outfits rather than individual pieces, thereby satisfying diverse user needs without proportionally increasing system complexity.
3Loss of time
If deep learning models are trained with limited data, then the training time is reduced, but the model performance degrades
Solution Approach 1:
The patent employs preliminary data augmentation techniques during the training phase, where synthetic training data is generated using geometric transformations and synthetic clothing-body pairings. This preliminary preparation of enhanced training data allows the model to learn robust features even with limited original data, improving model performance without requiring extensive additional training time.
4Ease of manufacture
If conventional methods are used, then the implementation is simpler, but the virtual fitting image shows distortion and artificial appearance
Solution Approach 1:
The patent replaces conventional image processing mechanisms with deep learning-based geometric matching and synthesis mechanisms. The geometric matching module uses learned geometric transformations instead of traditional image warping techniques, and the virtual fitting module uses generative models instead of simple compositing. This substitution maintains implementation feasibility while dramatically improving fitting image quality by reducing distortion and artificial appearance.
Data Source
AI summary
A method of a clothing virtual try-on service based on deep learning allowing a virtual try-on service application executed by at least one processor of a computing device to perform a process of the clothing virtual try-on service based on the deep-learning includes: determining a first clothes image including a first clothes object, a second clothes image including a second clothes object, and a model image including a model object; generating a first transformed clothes image by transforming a shape of the first clothes object to correspond to the model object; generating a second transformed clothes image by transforming a shape of the second clothes object to correspond to the model object; and generating and outputting a virtual fitting image obtained by synthesizing the first transformed clothes image and the second transformed clothes image to be virtually fitted on the model object.